{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling time series data is an important preprocessing step when using neural forecasting methods for several reasons:\n",
    "\n",
    "1. **Convergence speed**: Neural forecasting models tend to converge faster when the features are on a similar scale.\n",
    "2. **Avoiding vanishing or exploding gradients**: some architectures, such as recurrent neural networks (RNNs), are sensitive to the scale of input data. If the input values are too large, it could lead to exploding gradients, where the gradients become too large and the model becomes unstable. Conversely, very small input values could lead to vanishing gradients, where weight updates during training are negligible and the training fails to converge.\n",
    "3. **Ensuring consistent scale**: Neural forecasting models have shared global parameters for the all time series of the task. In cases where time series have different scale, scaling ensures that no particular time series dominates the learning process.\n",
    "4. **Improving generalization**: time series with consistent scale can lead to smoother loss surfaces. Moreover, scaling helps to homogenize the distribution of the input data, which can also improve generalization by avoiding out-of-range values.\n",
    "\n",
    "The `Neuralforecast` library integrates two types of temporal scaling:\n",
    "\n",
    "* **Time Series Scaling**: scaling each time series using all its data on the train set before start training the model. This is done by using the `local_scaler_type` parameter of the `Neuralforecast` core class.\n",
    "* **Window scaling (TemporalNorm)**: scaling each input window separetly for each element of the batch at every training iteration. This is done by using the `scaler_type` parameter of each model class.\n",
    "\n",
    "In this notebook, we will demonstrate how to scale the time series data with both methods on an Eletricity Price Forecasting (EPF) task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Time_Series_Scaling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install `Neuralforecast`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast\n",
    "!pip install hyperopt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `df` dataframe contains the target and exogenous variables past information to train the model. The `unique_id` column identifies the markets, `ds` contains the datestamps, and `y` the electricity price. For future variables, we include a forecast of how much electricity will be produced (`gen_forecast`), and day of week (`week_day`). Both the electricity system demand and offer impact the price significantly, including these variables to the model greatly improve performance, as we demonstrate in Olivares et al. (2022).\n",
    "\n",
    "The `futr_df` dataframe includes the information of the future exogenous variables for the period we want to forecast (in this case, 24 hours after the end of the train dataset `df`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>system_load</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>53.48</td>\n",
       "      <td>76905.0</td>\n",
       "      <td>74812.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>51.93</td>\n",
       "      <td>75492.0</td>\n",
       "      <td>71469.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>48.76</td>\n",
       "      <td>74394.0</td>\n",
       "      <td>69642.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>42.27</td>\n",
       "      <td>72639.0</td>\n",
       "      <td>66704.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>38.41</td>\n",
       "      <td>69347.0</td>\n",
       "      <td>65051.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds      y  gen_forecast  system_load  week_day\n",
       "0        FR 2015-01-01 00:00:00  53.48       76905.0      74812.0         3\n",
       "1        FR 2015-01-01 01:00:00  51.93       75492.0      71469.0         3\n",
       "2        FR 2015-01-01 02:00:00  48.76       74394.0      69642.0         3\n",
       "3        FR 2015-01-01 03:00:00  42.27       72639.0      66704.0         3\n",
       "4        FR 2015-01-01 04:00:00  38.41       69347.0      65051.0         3"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    'https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv',\n",
    "    parse_dates=['ds'],\n",
    ")\n",
    "futr_df = pd.read_csv(\n",
    "    'https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_futr.csv',\n",
    "    parse_dates=['ds'],\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that `y` and the exogenous variables are on largely different scales. Next, we show two methods to scale the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Time Series Scaling with `Neuralforecast` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most widely used approches for scaling time series is to treat it as a pre-processing step, where each time series and temporal exogenous variables are scaled based on their entire information in the train set. Models are then trained on the scaled data.\n",
    "\n",
    "To simplify pipelines, we added a scaling functionality to the `Neuralforecast` class. Each time series will be scaled before training the model with either `fit` or `cross_validation`, and scaling statistics are stored. The class then uses the stored statistics to scale the forecasts back to the original scale before returning the forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a. Instantiate model and `Neuralforecast` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we will use the `TimesNet` model, recently proposed in [Wu, Haixu, et al. (2022)](https://arxiv.org/abs/2210.02186). First instantiate the model with the desired parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from neuralforecast.models import TimesNet\n",
    "from neuralforecast.core import NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "model = TimesNet(h = horizon,                                   # Horizon\n",
    "                 input_size = 5*horizon,                        # Length of input window\n",
    "                 max_steps = 100,                               # Training iterations\n",
    "                 top_k = 3,                                     # Number of periods (for FFT).\n",
    "                 num_kernels = 3,                               # Number of kernels for Inception module\n",
    "                 batch_size = 2,                                # Number of time series per batch\n",
    "                 windows_batch_size = 32,                       # Number of windows per batch\n",
    "                 learning_rate = 0.001,                         # Learning rate\n",
    "                 futr_exog_list = ['gen_forecast', 'week_day'], # Future exogenous variables\n",
    "                 scaler_type = None)                            # We use the Core scaling method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model by instantiating a `NeuralForecast` object and using the `fit` method. The `local_scaler_type` parameter is used to specify the type of scaling to be used. In this case, we will use `standard`, which scales the data to have zero mean and unit variance.Other supported scalers are `minmax`, `robust`, `robust-iqr`, `minmax`, and `boxcox`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |                                                                                            …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "591faa8986cf43949bb30f0f11133ac7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |                                                                                                   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nf = NeuralForecast(models=[model], freq='h', local_scaler_type='standard')\n",
    "nf.fit(df=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.b Forecast and plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. The `Neuralforecast` class handles the inverse normalization, forecasts are returned in the original scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2007c78ee51645e0b2528e9164ea19ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>TimesNet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>39.523182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>33.386608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>27.978468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>28.143955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>32.332230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds   TimesNet\n",
       "0        BE 2016-11-01 00:00:00  39.523182\n",
       "1        BE 2016-11-01 01:00:00  33.386608\n",
       "2        BE 2016-11-01 02:00:00  27.978468\n",
       "3        BE 2016-11-01 03:00:00  28.143955\n",
       "4        BE 2016-11-01 04:00:00  32.332230"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 2 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(df, Y_hat_df, max_insample_length=24*5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "The inverse scaling is performed by the `Neuralforecast` class before returning the final forecasts. Therefore, the hyperparmater selection with `Auto` models and validation loss for early stopping or model selection are performed on the scaled data. Different types of scaling with the `Neuralforecast` class can't be automatically compared with `Auto` models.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Temporal Window normalization during training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temporal normalization scales each instance of the batch separately at the window level. It is performed at each training iteration for each window of the batch, for both target variable and temporal exogenous covariates. For more details, see [Olivares et al. (2023)](https://arxiv.org/abs/2305.07089) and https://nixtla.github.io/neuralforecast/common.scalers.html."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.a. Instantiate model and `Neuralforecast` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temporal normalization is specified by the `scaler_type` argument. Currently, it is only supported for Windows-based models (`NHITS`, `NBEATS`, `MLP`, `TimesNet`, and all Transformers). In this example, we use the `TimesNet` model and `robust` scaler, recently proposed by Wu, Haixu, et al. (2022). First instantiate the model with the desired parameters.\n",
    "\n",
    "Visit https://nixtla.github.io/neuralforecast/common.scalers.html for a complete list of supported scalers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "model = TimesNet(h = horizon,                                  # Horizon\n",
    "                 input_size = 5*horizon,                       # Length of input window\n",
    "                 max_steps = 100,                              # Training iterations\n",
    "                 top_k = 3,                                    # Number of periods (for FFT).\n",
    "                 num_kernels = 3,                              # Number of kernels for Inception module\n",
    "                 batch_size = 2,                               # Number of time series per batch\n",
    "                 windows_batch_size = 32,                      # Number of windows per batch\n",
    "                 learning_rate = 0.001,                        # Learning rate\n",
    "                 futr_exog_list = ['gen_forecast','week_day'], # Future exogenous variables\n",
    "                 scaler_type = 'robust')                       # Robust scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model by instantiating a `NeuralForecast` object and using the `fit` method. Note that `local_scaler_type` has `None` as default to avoid scaling the data before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |                                                                                            …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6a7aa2d840b43739b1f6287227dd1c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |                                                                                                   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nf = NeuralForecast(models=[model], freq='h')\n",
    "nf.fit(df=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.b Forecast and plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. The forecasts are returned in the original scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c17daa2fd0b340dbbebf2a0394abf7ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>TimesNet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>37.624653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>33.069824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>30.623751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>28.773439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>30.689444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds   TimesNet\n",
       "0        BE 2016-11-01 00:00:00  37.624653\n",
       "1        BE 2016-11-01 01:00:00  33.069824\n",
       "2        BE 2016-11-01 02:00:00  30.623751\n",
       "3        BE 2016-11-01 03:00:00  28.773439\n",
       "4        BE 2016-11-01 04:00:00  30.689444"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REUWvsrKycC8hajE6kmJGRXM3AKChwxbmlRARERER0WhQ32GD1eEGANS1W8O8GiIiIiIiCgcW2igmdNqcaOqyA2ChjYiIiIiIQqOsqUv7//XchxARERERjUostFFMKG/q1v5/Qyc3uEREREREFHwH+xXa2NFGRERERDQasdBGMaG8ubfQVs/IFiIiIiIiCoGyPgf+6tp54I+IiIiIaDRioY1iQkWfQlu71QmrwxXG1RARERER0WjQt6ONEfZERERERKMTC20UEw42d/X770bGRxIRERERUZCVNfZJ1uiwQpblMK6GiIiIiIjCgYU2ignlzT39/punSYmIiIiIKJhkWe7X0eZwyWjpdoRxRUREREREwbFkyRKcffbZ4V5GxGKhjWKCiI7UScp/s9BGRERERETB1NhpR5fdBUkCUkwGAEpXGxERERFRNJEkadi3u+++G3/5y1/w8ssvh3Wdd999NyRJwm9+85t+79+4cSMkSUJZWZnHz3X88cfj+uuvD9jaWGijqOdyy6hsUQptU/LMAIAGRkcSEREREVEQiW62fEsC8lMTAAB17dyHEBEREVF0qamp0d6eeOIJmM3mfu+76aabYLFYkJqaGu6lIj4+Hi+++CL27NkT7qX0w0IbRb2ath44XDLi9DpMz7cAAOq5wSUiIiIioiAqa1IO+5VkJiLbbAIA1Lezo42IiIiIesmyjC6XKyxvns4Pzs3N1d4sFgskSer3vuTk5AHRkccffzyuvfZaXH/99UhLS0NOTg6ef/55dHV14dJLL0VKSgomTJiATz75pN/X2rp1K0477TQkJycjJycHF198MRobG7WPv/3225gxYwYSEhKQkZGBRYsWoaurN6590qRJOOGEE/DHP/5x2O9puK+zZMkSfP311/jLX/6ide150w03GINfn00UAcrVDW5hWgJyLPEA2NFGRERERETBJTraijOSYHO4AQD1jLAnIiIioj663W6MX7ElLF9733EzkKTXB+35X3nlFdxyyy1Ys2YN/v3vf+Oqq67Ce++9h3POOQd/+MMf8Pjjj+Piiy9GeXk5EhMT0draihNPPBG/+tWv8Pjjj6Onpwe33norfvKTn+DLL79ETU0NLrroIjz88MM455xz0NHRgW+++WZAwfDPf/4zFixYgHXr1mH+/PkD1jXS1/nLX/6C3bt3Y/r06bj33nsBAFlZWX79WbDQRlGvXJ3PNiYjEdkpyklSzmgjIiIiIqJg0jraMhLR0u0AwI42IiIiIho9Zs2ahdtvvx0AcNttt+HPf/4zMjMzccUVVwAA7rzzTjzzzDPYvHkzjjjiCDz99NOYM2cOHnjgAe05/vGPf6CoqAi7d+9GZ2cnnE4nzj33XBQXFwMAZsyYMeDrzp07Fz/5yU9w6623YtmyZQM+PtLXmThxIuLi4pCYmIjc3NyA/Fmw0EZRTyu0pScii4U2IiIiIiIKgb4dbXH6HgDsaCMiIiKi/hJ1Ouw7bmCxKFRfO5hmzpyp/X+9Xo+MjIx+hbGcnBwAQH19PQBg06ZNWL58OZKTkwc81759+7B48WKcdNJJmDFjBk455RQsXrwY559/PtLS0gY8/v7778eUKVOwdOlSZGdn9/vYSF9n4sSJvn3Dw2ChjaLeQRbaiIiIiIgohGRZxoFGpdBWkpEEl1uJs2GhjYiIiIj6kiQpqPGN4WQ0Gvv9tyRJ/d4nSRIAwO1WYtY7Ozvxox/9CA899NCA58rLy4Ner8fnn3+OVatWYenSpXjqqafwxz/+EatXr8bYsWP7PX78+PG44oor8Pvf/x4vvvhiv4+N9HWCgYU2inoVfQttyb2FNlmWtX/MREREREREgdLa7UCH1QlA2Yd02pToyDpGR0Ytt1uGTsf9IxEREVGwzJ07F++88w5KSkpgMAxempIkCUcffTSOPvpo3HnnnSguLsZ7772HG2+8ccBj77zzTowfPx7/+te/vP46cXFxcLlc/n9TquD2DhKFQN8ZbaKjze5yo73HGc5lkY8aOmxwutzhXgYRERER0ZDK1NjIXHM8EuL0yE6JB6B0tB06rJ0i3+r9TZh1z1L8a015uJdCREREFLOuueYaNDc346KLLsLatWuxb98+fPbZZ7j00kvhcrmwevVqPPDAA1i3bh3Ky8vx7rvvoqGhAVOmTBn0+XJycnDjjTfiySef9OrrAEBJSQlWr16NsrIyNDY2al13vmKhjaJaW48Drerg8THpiYg36mGOV6rUDZ08TRpt9jV04vAHvsDv/r0x3EshIiIiIhrSwSblsF9xRiIA9B74c/LAXzT6clc9OmxOfLmzPtxLISIiIopZ+fn5+Pbbb+FyubB48WLMmDED119/PVJTU6HT6WA2m7FixQqcfvrpmDhxIm6//XY8+uijOO2004Z8zptuumnALLaRvo74PL1ej6lTpyIrKwvl5f4duGJ0JEU1ERuZmWxCYpzy1zkrxYR2qxP1HTZMyE4J5/LISxvLW+GWlf8lIiIiCgaXy4W7774br7/+Ompra5Gfn48lS5bg9ttv12LHZVnGXXfdheeffx6tra04+uij8cwzz6C0tDTMq6dIITraSjKSAADxRj0sCUa09ThQ12GFJdE43KdThClT5+1xxh4RERGNdkuWLMGSJUsGvP/ll1/u999fffXVgMeUlZUNeN+haQ+lpaV49913B/3aU6ZMwaeffjrk2u6++27cfffd/d5nNpvR0NAw4LHDfR0AmDhxIr777rshP+6tsHa0uVwu3HHHHRg7diwSEhIwfvx43Hffff3+8GVZxp133om8vDwkJCRg0aJF2LNnTxhXTZFEi41MT9DeJ06TNnCTFHUqW3oA9M7YIyIiIgq0hx56CM888wyefvpp7NixAw899BAefvhhPPXUU9pjHn74YTz55JN49tlnsXr1aiQlJeGUU06B1crEBFJoHW2Zidr7stV9SH079yHRpqxR+XnWc8YeEREREfkgrIU2bnLJX72RLUna+7LU+QgstEWfyhbl52l3udHW4wjzaoiIiCgWrVq1CmeddRbOOOMMlJSU4Pzzz8fixYuxZs0aAMpBvyeeeAK33347zjrrLMycOROvvvoqqqur8f7774d38TGisqUbn2ypieqDVYd2tAFAtlkttHVwrxpN3G4ZB5uVn2dDJw/8EREREZH3wlpo4yY3vHrsLizbUQeny79Bf+EkOtqK0ntPkmYls6MtWlW19mj/n7EtREREFAxHHXUUli1bht27dwMANm3ahJUrV2q5/wcOHEBtbS0WLVqkfY7FYsHhhx8+bLSIzWZDe3t7vzca3I3/3oSr/rkB726oCvdSfHbojDYAyFEP/NWxoy2q1HVYYXUoe2KHS0ZLNw/8EREREZF3wlpoC8Ymlxtczz32+S5c/so6PL18b7iX4rMKLTqyT2SLmYW2aCWiIwFG7hAREVFw/P73v8eFF16IyZMnw2g0Ys6cObj++uvx85//HABQW1sLAMjJyen3eTk5OdrHBvPggw/CYrFob0VFRcH7JqJYU6cNaw82AwBe/a4svIvxUVuPA81ddgCHJGuwoy0qidhIgT8/IiIiIvJWWAttwdjkcoPruaXb6wAAb64pj9quNhHx0fckqdbR1slCTTRxuWVU9+to4waXiIiIAu+tt97CP//5T7zxxhvYsGEDXnnlFfzf//0fXnnlFb+e97bbbkNbW5v2VlFREaAVx5Zv9jRCJPNtqmzDlsq28C7IB+VqN1tmsgnJJoP2/my1o43JDNFFxIAK7EgkIiKiQGAcdfTz5mcY1kJbMDa53OB65mBTlxZ3Utduw/JdDWFekfccLjeqW5ViTN+OtqwUdrRFo/oOK5xuuc9/8+dHREREgXfzzTdrB/5mzJiBiy++GDfccAMefPBBAEBubi4AoK6urt/n1dXVaR8bjMlkgtls7vdGAy3fVQ8AMOgkAMDr3x8M53J80jufLbHf+3NER1s7D4xFk0MLbfz5ERERkT+MRiMAoLu7e4RHUqQTP0PxMx2OYcRHBFHfTS4AzJgxAwcPHsSDDz6ISy65pN8mNy8vT/u8uro6zJ49e9DnNJlMMJlMQV97tPtmT2O///732nKcPDVniEdHpppWK1xuGSaDDtkpvT9zFtqiU9/YSACo4waXiIiIgqC7uxs6Xf/zhnq9Hm63kvAwduxY5ObmYtmyZdqeo729HatXr8ZVV10V6uXGFJdbxordygG/G06eiEc+24X/bqrCH06fAkviyJvXSHGwSaRqJPV7PzvaolNZo/LzlCRAlvnzIyIiIv/o9Xqkpqaivl45YJaYmAhJksK8KvKGLMvo7u5GfX09UlNTodfrR/ycsBbauMkNH7HBPXdOAd79oQpf7qxHbZsVuZb4MK/McyI2ckx6/19WotDW1GWHw+WGUR/Wxk3yUNUhhTZucImIiCgYfvSjH+FPf/oTxowZg2nTpuGHH37AY489hssuuwwAIEkSrr/+etx///0oLS3F2LFjcccddyA/Px9nn312eBcf5TZVtqKl24GUeAOuPG4cPtxUjZ21HXhnQyUuO2ZsuJfnsTI1GWTojjYbZFnmDZUoIZJepuSasb2mnQc2iYiIyG+igUgU2yg6paamDptq0ldYC23c5IaHw+XGd/uaAABLji5BZUsP1pQ14z/rKnDtSaVhXp3nypuVDVHf2EgASE+Mg14nweWW0dRpj6ri4WhW2aL8PJPi9Oiyu9DA2QhEREQUBE899RTuuOMOXH311aivr0d+fj5+/etf484779Qec8stt6CrqwtXXnklWltbccwxx+DTTz9FfDyvK/3xlRpXf1xpFox6HX5+RDHueH8rXl99EJceXRI1hSmtoy1z8I62HocLHTYnzPHR06U3WrndshYdedjYdGyvaWeyBhEREflNkiTk5eUhOzsbDocj3MshHxiNRo862YSwFtq4yQ2PTRWt6LA5kZZoxLR8Cy48rAhryprx73UVuOaECdDpomODK4aQjznkJKlOJyEzOQ517TY0dNhYaIsSIjpy9phUfLu3CfUd3OASERFR4KWkpOCJJ57AE088MeRjJEnCvffei3vvvTd0CxsFvlLnsx0/KQsAcM6cAvz54x3Y39CF7/Y14agJmeFcnseG6mhLiNMjxWRAh82J+nYbC21RoK7DCqvDDb1OwrziNLy8qozJGkRERBQwer3eq2INRa+wZuqJTe7BgwfR09ODffv24f7770dcXJz2GLHJra2thdVqxRdffIGJEyeGcdXRb4U6n+3oCZnQ6yScNj0PKfEGVLb04Nt9jSN8duQYqqMN6DOnrZPFmmhR1aoU2uYUpQFgdCQRERFRLGnosGFzZRsAYKFaaEs2GXDO3AIAwGvfHwzb2rzRZXNq0YLF6UkDPp4t4iN5aCwqlDUqe8rCtATkp4oZe/zZEREREZF3OLxqFPpmT29kC6CcvDxnjrLB/dfairCty1vDFtqS1UIbizVRQ3S0zS1OBQB0213otDnDuCIiIiIiChQxI3p6gVmLWASAXxxRDABYur0uKiL7xDyvtEQjLIkDO9bE91bPGPSoIGIjSzKS+v3sZFkO57KIiIiIKMqw0DbKtHU7sKmiFQBw7MTeaJafLigCACzdVoumzsjfFMqyrEVHFmcM3dHGDW50cLtlVKmFttLsFCTFKS3V9VFws4WIiIiIRrZcjY08YVJ2v/dPzjVjQUkaXG4Z/1oT+Yf+tPlsGQO72QB2tEUbUWgbm5mk7SFtTjfae3jgj4iIiIg8x0LbKPPtvka4ZaA0Oxl5lgTt/dPyLZhZaIHDJePdDVVhXKFnWrsd6FC7nQrThouOZKEtGjR22mB3KbMR8izxyDaL2Bb+/IiIiIiindPlxjdqfL2Yz9aX6Gp7Y81B2JyukK7NW0PNZxNyzOxoiyZljaJwmoh4ox6WBKVLkYVSIiIiIvIGC22jjIiNPLZ04AZXdLX9a215xEdliNjIHLMJ8caBAyVF7AejI6NDhdrNlmuOh0Gv6+1I5M+PiIiIKOptrGhFW48DqYlGzFbn8fZ16vRc5JhNqGu34ZVVZaFfoBdq25Tr1vzUhEE/nq1ex9bxOjYqiCjQkkylQzGb+xAiIiIi8gELbaOILMtYsVs5Sdo3NlL48ax8JBj12NfQhRXqidNIVdEy9Hw2oE9HGzdIUaFS/XkWpCk3LLQNLqMjiYiIiKKeiI08tjQLep004OMmgx43LZ4EAHhq2d6IjrJv6rIDANKT4gb9eBavY6OG2y33m9EG9EZ/RsO8QCIiIiKKHCy0jSIHGrtQ1dqDOL0Oh49NH/DxlHgjLjpsDADg4U93wu2O3K62RrWA1neQel+MjowuVa3KyeBCrdDGjkQiIiKiWPHVLiVV44RBYiOF8+YWYlq+GR02J/6ybE+olua1ZrXQlpE8eKFNREeOhuvYqtaeiN4zjqS+wwarQ4mvP3Qfwo42IiIiIvIGC22jyIrdygZ3wdg0JMYZBn3Mb0+cgGSTAduq2/G/LTWhXJ5XmrsdAIC0JOOgH89KZkdbNKlUoyML1QieHDMjW4iIiIhiQX27Fduq2wEAx00cutCm00n44xlTAAD/XF2OvfWdIVmft5q1jjbToB/XoiNjvCNq+a56HP3nL3HFq+vgdLnDvRyfHFDnsxWmJcCoV26NiI42ztgjIiIiIm+w0DaKiAHkg81nE9KT4nDlceMAAI8u3QW7MzI3TS1ig5s4fGRLt92FTpszZOsKh2/3NqJCnVkXrapEoS1NiQLVNrgcQk5EREQU1b5WD/vNKrQgM3nw4pRw1PhMLJqSA5dbxoMf7wjF8rwmoiMzhoiOzFY72rpifB/yv03KocxlO+tx1wfbIn7G92AOjY0E+na0cR9CRERERJ5joW2UsDvd+G5/EwDg2NKB89n6uvyYschMNuFgUzf+tbY8FMvzWnO3ssFNG2KDm2QyIDFODyC2u9q2Vbfh5y+sxo+fXhnVxTYxo+3QyJY6niQlIiIiimrl6jXqzMJUjx5/2+mTYdBJWLazHisjbG60LMvagb+hoiOTTQYkqfuQWJ3TJssyvtnToP33P1eX4/lv9odxRb4RhbaxmX0LbexoIyIiIiLvsdA2SmyqbEW33YXM5DhMyTUP+9gkkwG/O2kCAODJZXvQFYEnMVtGGEIO9G6SYrnQtvZAMwCgpduBX72yLipPzcqyrM1oK9AKbRwiT0RERBQLxPVpSvzg0fWHGp+VjF8cUQwAuP+j7XBF0Ayw9h4nnOp6ht2HmGN7ztfuuk7Ud9gQb9Th1lMnAwAe+HgnPtocuaMHBlOmRkcWZyRq79P2IexoIyIiIiIvsNA2StS2KRuFCdnJ0OmkER9/4WFjUJyRiMZOO15ceSDYy/NasweFtqxRUGjbUtWu/f9ddR24/l8/RNTNCE80ddlhdbghSUCepX9HW7vVCavDFc7lEREREZEfOq1KoS3J5FmhDQB+d1IpzPEG7KztwFvrKoK1NK81din7imSTASaDfsjH9RZrYnMfIrrZDh+bgauOH48lR5UAAG54ayPWH2wJ48q8c7BJ6bYs6dPRlhPjRVIiIiIiCg4W2kYJ0ZWW7OEG16jX4f8tngQAeG7FfjR1RtZGo0VERw4xow3oW2iL3dOIW6vaAAC/PWEC4gw6fLGjHv+3dFeYV+WdSnU+W645HnEG5VeSOcGg/f9YLpQSERERxbouu3f7EECJh7/upFIAwD0fbsOG8sgo3nhy2A/o09EWo+kMvbO/lZEEd5w5FYum5MDudOOKV9dpsfCRzO2WB5/RZh49s76JiIiIKHBYaBslxCYhMc7zDe6ZM/IwvcCMTpsTTy/fG6yleU2ZjeAAMEJHW3JsnyTtsbuwp74DAPCLI4rxyPkzAQDPfLUP7/1QGc6leUVsxAtSE7T3SZLE2BYiIiKiGNBpU9IJvOloA4BLjirB8ZOyYHW4cdnLa7FXve4Np6ZODwttMdzRZnW4sPqAmP2dBQDQ6yQ8edFszCiwoLnLjsc/3xPOJXqkvsMGq8MNvU7S5kQDyn5ZFIXrYrRQSkRERESBx0LbKNFt936Dq9NJWub+y6vKsHRbbVDW5q1OmxN2lxuApx1tsbfBBYAdte1wy0Bmsgk5ZhPOml2Aq48fDwC49Z0t2FTRGt4FeqhK7Wjru8EFOIiciIiIKBZ4m6whGPU6/O3nczGrKBWt3Q788sU1Whx+uIiOtowRCm055tidN7z+YAusDjeyU0yYmJOsvT8xzoD7zp4OAHh/YxUqmiO7q+2AOp+tKC0BRn3/2yLchxARERGRt1hoGyV6N7hDzxIYzLGlWfj54WMgy8Dv/rURmytbg7A674hutgSjHglxw81GUCJbGiIs9jJQRGzk9AIzJEmZu3fT4klabMsDH+8I5/I8VqkV2hL7vV/8/GLxJDARERHRaCFmtHlbaAOU4s1LSxZgXGYSqtusuOQfa9DW7Qj0Ej3WrM5oG7mjTbmOrYvBQs0KdT7bsaVZ2h5EmF2UimNLM+Fyy/jbV/vCsTyPHVRjI4v7xEYKIj6SyRpERERE5CkW2kYJX6IjhXt+PA0LJ2ahx+HCZS+HP3O/uduzyJZY72jbUqkU2mYUWLT36XQS7jt7Gox6CasPNOOHCJlnMRwtOvLQjjZucImIiIiintiHJHl54E9IT4rDK5cdhuwUE3bVdeCKV9fB6nAFcokeaxIz2pI9jY6MvevYlYfMZzuUmK339voKVLf2hGxd3jqgFtrGZg5SaBMHNmN0H0lEREREgcdC2yghoiN9OUlq0Ovw9M/mYHJuCho7bbjs5bVot4bvJGmLusFNSzIO+7iYL7RpHW2Wfu/PsyTgrNkFAIBnv47sk6QAUNXK6EgiIiKiWNVl972jTShKT8Qrlx2GFJMBa8qa8eaa8kAtzysiOjIzyTTs47LNsZnM0Nhpw7bqdgDA0RMGL7QtKEnH4WPT4XDJ+HsE70XKGkVHW+KAj4l9CGe0EREREZGnWGgbJXpPkvq2wU2JN+KlSxcgx2zC7rpOXP36BjjUOWmhJja4w81nA3oLbY2dNrjcctDXFUpWhwt76jsB9O9oE36zcBwAYOn2Ouxr6Azp2rwhyzKjI4mIiIhilCzLWoS9r/sQYUqeGZceXQIA2F3X4e/SfCL2IZ4ma3RYnWHrvguGb/cq3WxT88za9zgY0dX25tqKiJ1Td7BJSdUoGayjTUvW4D6EiIiIiDzDQtso0eVnZAugdEq9eMkCJMbpsXJvI/66fG+glueVlm7PhpBnJMVBkgC33LspjhU7azvgcsvISIpDniV+wMcnZKdg0ZQcyDLw3Nf7w7BCz7R2O7Ruy0O/jyxucImIiIiims3phsOlHHhLjvev0AYAhenKwSxxUCvUmjo9i440xxtg1Cvzy2JpH7JitxobOXHwbjbhqPEZmDsmFXanG89/E3l7EbdbRpkaHVkyyIy2HNGRyGQNIiIiIvIQC22jRJdazEjyYUZbX9MLLPj9aZMBAGsONPu9Ll9oHW0jFNoMep1WjIu12A8RGzmtwDJgCLlw1fFKV9u7P1Siti0yv39xkyQ7xYR4Y/8icLYW/RmZayciIiKi4YnDfoD/+xCgN2q8Kkyzv8Q+ZKQDf5IkaV1vsVJok2UZ3+xpAAAcV5o17GMlScK1Jypdba9/X46mzsgqWNV32GB1uKHXSQPi64HejkRPZuzZnC7c+vZm3P+/7dhbH7lJIkREREQUXCy0jRJik5voR0ebMCknBQDCNtxai2wZIToSAHLVLqlILTT5amulUmibUWAe8jHzitOxoCQNDpeMf3x7IFRL80plixLZMtgGV0RHNnbawxZTSkRERES+67Iph/0SjHrodYMfDvNGYarS0VbV0gNZDm00vCzLaOpSCkYjRUcqj+mNsY8Fe+o7Ud9hg8mgw7zitBEff/ykLMwosKDH4cKLKyNrL1Kh7kHyU+Nh1A+8JaJF2HvQ0bZidyP+va4CL6w8gEWPfY2f/P07/HdjVUxFhhIRERHRyFhoGyW6bf4PIRfyU5WiSHWbFe4wzD7ztKMNAHLNylprYqyjbWu1KLQNnM/W128WjgcAvLG6HG09jqCvy1viNHJB2sAh5BlJcdoNmVi5QUFEREQ0mvg7J/pQuZZ46CQlkrKxM7SdYh02pxaDmZE09HwyISPGOtpW7Fa62Q4flzEgiWIwkiThtydOAAC8+t1BtFsjZy8iDozmWQYe9gN6Z7R12JzosQ9fMKttU54rJd4AnaSkvvzuXxtx5IPLsP5gSwBXTURERESRjIW2USKQm9xcSzwkCbA73WgKw8ZRzGjz5CRpfqpyGrEmTN13wWBzurQB8NNHKLSdMCkbE3OS0Wlz4vXvD4ZieV4R0ZGDdbTpdBKyktXYFs5HICIiIoo6Yg+SEoD5bAAQZ9Bp87NEMkKoNKuFvQSjHglxIxeaYi068ps9yny240qHn8/W18lTcjA+KwmdNic+3FQdrKV5rUZNO8kfZNY1AKSYDIg3KrdKRoqPFPOkz55dgG9/fyKuX1SKrBQTWrod+HRrTQBXTURERESRjIW2UUCW5YDNaAMAo16HHDVOIxzxkVpHmwfRkeKUYk0MRUfuqu2AwyUjLdGIgtTBT2EKOp2EXx+ndLW99G1ZxEWYDBcdCfSeJhUbWCIiIiKKHl3aYT//4+sFcf0b6jlt4oChJ4f9ACAjOa7f50Uzm9OF1QeaAADHjjCfrS+dTsKFC8YAAN5aWxGUtflCjBXIG2IvJUmSVtAdaR8iZoHnmE3IsyTg+kUTccmRxQAQkYkiRERERBQcLLSNAjanGy414jFQm1zRKRaOQltLt7Jh8aajLVzz5IJhS5USGzm9wAJJGnnWxY9n5yPfEo/GThs+3hJZpypFR9tQBcNsLwaRExEREVFk0VI1AnDYTxAHtMR1ZKiIw36igDYSER3ZFAMR6OVN3bA63EiJN2BiTrJXn3vO3AIYdBI2VbZhR017kFboHbE3HKqjDejdh9SNMIKgTk3eyDb3PpclwQiAhTYiIiKi0YSFtlFAnCQFgMQAbXLzw3SS1OWW0dotZrQZR3x8LHa0be1TaPOEUa/DT9WTpO9sqAzaunxRpUVHDpzRBgBZXgwiJyIiIqLI0hXAOdFCgVpoqwp5oU25HvW0oy1dneMWC9GRDWqxMDvF5NFBv74yk01YNCUHAPDvCOlqE3vDoWa0AUC2h/sQ0fEmCnMAYFYLbe09zkE/h4iIiIhiDwtto0CXTYkLTDDqodd5tzEaiuhAqm4NbQGrvccBtTnPw+hIZYNU22aFLMvBXFrIbK1SToLO8LDQBgDnzi0AAKza1xQx3X0dVgc61JsvovPwUL0dbSy0ERERUeCUlJRAkqQBb9dccw0AwGq14pprrkFGRgaSk5Nx3nnnoa6uLsyrjj6ioy05QDPagN4DWqGe0SYiIDOSTCM8UiEKcrEQHdmozqfLTPbsez/UTw8rAgC8v7EKNmf4o+xr2pT9UN4QexAAyPJwH1KvRUeyo42IiIhoNGOhbRTosovZCIHb4OZrhbYQnyRVu9nM8QYY9SP/9c0xx0OSALvLHRObXLvTjV21HQC8K7QVpSfisLHpkGXgvR+qgrU8r4jZCJYE45CdlmJGWwOjI4mIiCiA1q5di5qaGu3t888/BwBccMEFAIAbbrgBH374If7zn//g66+/RnV1Nc4999xwLjkqadGRgexoC1OyRnOnd9GRmerjYqGjTcRf+lpoO640C3mWeLR2O7B0W3gL1janSysc5g/T0abNaBsmOtLu7N1jstBGRESHkmU5Zg79E9HIWGgbBYIxhFwrtLWFZzaCp5EtcQadtiGsCXH3XTDsruuA3eWGJcGozafw1PlzCwEo8ZGR8EJfq25ac83DzUbwbAg5ERERkTeysrKQm5urvf3vf//D+PHjsXDhQrS1teHFF1/EY489hhNPPBHz5s3DSy+9hFWrVuH7778P99KjSjCiIwv7REeG8prW232I1tHWGf2Ftkat0ObZ934ovU7C+fOUvchb68IbHykO+5kMOqQmDj2KwJNkDfHnYtRLSOvzXBYtOpKFNiKi0Wp3lxVTV27F1dsPRsQ9OCIKvrAW2hjZEhrBGEIuov5C3tHWJeazeb7JE0OuQ10UDIYt6ny2GQUWr+cjnDYjF/FGHfY3dGFTZVswlucVMRsh14Mh5JzRRkRERMFit9vx+uuv47LLLoMkSVi/fj0cDgcWLVqkPWby5MkYM2YMvvvuuyGfx2azob29vd/baNepRtgHdh+iFNq67C60doeukNHkZaFNREx22pwREZfoj8YO/6IjAeCCeUp85Dd7GlHRHNrYz77E6IP81IRh91MiWaN+mGSNOvXgYHZKfL/nEoW2DpsTLjdvrhIRjTayLOOOPVVocbrwXn0r3qtvDfeSiCgEwlpoY2RLaHTblY1dQIeQqxvcxk47rI7QbRxbxAbXg/lsghhyXRMhs8n8sVUttE0rMHv9uSnxRpw6LRcA8M76yoCuyxfiNOmwHW3qBrex0wY3N6lEREQUBO+//z5aW1uxZMkSAEBtbS3i4uKQmpra73E5OTmora0d8nkefPBBWCwW7a2oqCiIq44OwZjRFm/Ua/OzQhkf2dSlHPzK8LDQZk4wwKDOx472+Ejte/ej0DYmIxFHT8gAAPwnjHsRbT7bMIf9AM+SNerUw4BizyKYE3q729jVRkQ0+ixtasfXLR3af9++pxINdr4eEMW6sBbaghHZwpOkA4kNbmIAoyOVuVrK84nOpFAQM9q86WgTHVM1w+TrR4utfTrafHGuGh/5wabqsJ+s1aIjh9nkZiabIEmA0y1rP3siIiJPybKMv5TV4YXKhnAvhSLYiy++iNNOOw35+fl+Pc9tt92GtrY27a2iIrwReZGgNzoycPsQoPfQX2VL6DqjxIw2TzvaJEmKmfjIhk7R0eZbdKTwk/lK8fntdRVh6/QSe9e8YeazAUCOWjxr7XYMebBUzJEWKRyCUa/T9sqc00ZENLrY3G7cvbcKAPCboixMTYpHs8OFO/ZUhXllRBRsETOjLVCRLTxJOlB3EIaQS5LUO6cthCdJW7yMbAF6Yy6jfUabLMvYVaeciJmW71uh7egJmcg1x6Otx4HlO+sDuTyv1Wqb3KELbUa9TuteZHwkERF567/1rXjwQA1u31OFN2qawr0cikAHDx7EF198gV/96lfa+3Jzc2G329Ha2trvsXV1dcjNzR3yuUwmE8xmc7+30a4zCPsQAChIE4W20OxDZFnWoiNFJKQnxJ4l2jvaGtWurswU3zvaAOCUabmwJBhR3WbFyr2NgVia10RHm9gjDsWSYEScQbld0jBEV5voaMsZJKFDxEey0EZENLq8WNmIAz12ZMcZcFNJLh6fMgY6AO/Xt+KzxvCPcSGi4ImYQlugIlt4knSgLhEdGcDZCEDvfIRQRrY0dykblTRfoiOjfEZbW48DVocbwMgbw6HodRLOnlMAAHh7fXhP04hCW84IsS0iGqhumPkIREREh+pyuXDPvmrtv2/bXYmN7eGbC0SR6aWXXkJ2djbOOOMM7X3z5s2D0WjEsmXLtPft2rUL5eXlOPLII8OxzKjVFaRCW2GIC23ddhdsTuU6PN2Lrq4M9bEiejEaKUVGtdDmRZFxMPFGPc5R9yJvrQ3PPl0cvhypo02SJGQlizltQxXaBu9oA3oLbe1WFtqIiEaLBrsDj5cp96z/MC4PyQY9ZqUk4qox2QCAW3dVos3hDOcSiSiIIqbQFqjIFp4kHSgY0ZEAUKAWe0La0dYtOtqMIzyyV762zugu1IgTk2mJRpgMvv8sz5urbG6/2lWPps7wbfpFdOSI8xHUE6IN7GgjIiIvPHmwHjU2B4ri47AowwybW8blWw+gyc7NLSncbjdeeuklXHLJJTAYegtBFosFl19+OW688UYsX74c69evx6WXXoojjzwSRxxxRBhXHH3EPiQl0IW2EB/4Ex1pcQYdkuI8vw5PVwtT0Rwd2WV3aYf9MlP8i44EgLNmK/v9b/c1QpZDHx9ZLVI1PDi4KGav1Q8xgkAU4LIH6Wgzs6ONiGjU+fP+GnS43JiVkoCf5KZr77+pJBdjE+JQa3fgvn01YVwhEQVTRBTaAhnZQgN1a7MRAtzRZgl9dGSTFh3p+WlKcVqxrt0atlkAgSBOTA4WTeKN0pwUzCy0wOmW8cGm6pE/IQisDpd2wyJ3hO9HnBCtZ0cbERF5qKzHhmfKlYjkeyfk429TizEuwYQqmwNXbS+DKww3dynyfPHFFygvL8dll1024GOPP/44zjzzTJx33nk47rjjkJubi3fffTcMq4xuwetoSwQQuo42sQfJTIqDJEkef15GDERHitjIxDg9EgOQkDI13wyDTkJrtyOks74FLTpyhI42oHcWYHnz4N3Qw+3PGB1JRDS6bOnoxhs1zQCA+yYUQNfneiFBr8Ojk8YAAF6vacKqls6wrJGIgisiCm2MbAmuTpsSHRnoDW7vjLbQbZB6Z7R53tGWnWKCTgKcbjmsHVz+0qJJ/Cy0AcB5cwsBAO9sqPT7uXwh5q3FG3XaJnQoYhB5HTvaiIjIQ3fuqYJdlrEwLQWnZlpgNujx4vQSJOh0WNHSiT/v50lSAhYvXgxZljFx4sQBH4uPj8df//pXNDc3o6urC++++y4P+/mgM0gH/sSMtqqW0MTBNqvRid7ERgKxUWgTsZEZXn7vQzEZ9JiQnQwA2F7dHpDn9FSP3YXWbqXw5UlH29jMJABAWVPXoB/XOtqGiY5koY2IKPbJsow791ZBBnB2dioOS00e8Jij0pJxUZ7S5fZWbXOIV0hEoRD2QhsjW4JPO0nqRcyJJ3oLbSGMjlQ3qd7MaDPodchOUeMjw3BqMlDERi7HzyHkAPCjWUpky9aq9rBs/MVJ0lxz/IingkXHW90QkS1ERER9LWtqx9Kmdhgk4L7SAu11ZkpyAh6fXAQAeKq8Hp82cBg5UTA5XW4tcjDQB/5Ep1G71RmSGVgi+tGbVA2gtzDXGMXRkQ0dajdfsv97EGFqvjLeYVuIC23V6h4kKU7vUZypKLQdaBxYaLM73do+ih1tRESj22eN7fiutQvxOgm3jx96JNIpGRYAwMYOzo0mikVhL7QxsiX4uuzBiWwp6DMbIRT5+nanGx1q0TA9ybsTleLEYk0Ii4KBFqjoSED58yvOUCJ3dtaGdoML9M5nyx1hPhvQ+/2y0EZERCOxu924c08VAODywixMTOr/OnN2ThquLMwCAPytoj7k6yMaTbrUVA0ASArwrOgkkwFpiUohoyoE8ZGioJLh5R6kt6MtepMZGtVEkIAW2vKUQtv2mtAeeKhpFfPZEjyKAC0RHW2NA2+INqh/Lka9pP1d7EsU2tpZaCMiimkOt4z79ytjWX5dlI3C+KGvFeaYlftwu7qs6HS6hnwcEUWnsBfaGNkSfKKjLRCZ+n3lWEyQJMDW5zRfMLV2K19DJwHmeM+jI4E+8+SiuKOtt9AWmE3u5NwUAMDOmo6APJ83atWfw0jz2YDeQlstC21RY9W+RvxnXUVYBtwT0ej2fGUj9vXYkBVnwE0lg18zLinIBABs6uiG3e0O5fKIRpVO9bBfnF4HkyGwhTagd05bKAtt3h72Ex1wUR0d2Sk62gITHQn0drRtrwlPR1ueB4f9AGBshlJoq223osfe/4ZovYj1Txk8ocMcr+y9Y6Gj7eMtNTjqwWVYV8aoMyKiQ71R04S93TakG/W4Zkz2sI/NMRlRYDJCBrC5I3obAYhocGEvtFHwidOkgZ6NYDLokaWebAzFnLbm7t7YSJ3O8yHkQO9mKro72tQZAAHoaAOAybnKBje8HW0jDyEXXW8NHTY4XbwhGulkWca1b/yAm9/ejO/2NYV7OUQ0itTaHHisrBYAcPu4fKQMcWN/bEIc0o162NwytnZG73UBUaTT4usD3M0miHSNyhDMaWvs9K3QJuaaNUVxoS0YHW3T8pTorIrmnpAWokRHW74HexAASEuK0zrTDp3T1rs3G/zPxZIYO9GR/9tcjeo2Kz7ZWhvupRARRZROpwuPHFB+N/6/klyYPThYNFvtatvQPvj8TyKKXiy0jQK90ZGB3+Tm94mPDDZxEjTNyw0u0FusqYnirqj6AEZHAn062mrD19HmyWnSzGQT9DoJbjm6b1KMFm09Du3n9Maa8jCvhohGk/v3VaPL5cY8cyIuyE0b8nGSJGGuWelSWN/G+QhEwdJpC058vVCQFsp9iFJU8TU6ssPqhC1KI6KCUWizJBq1QunOEHa11barHW2pnu+nSoaY01bfoe7NUgZ/rlia0Vapdo3ua+gM80qIiCLL3yrq0ehwYlyCCb/Mz/Toc+akKIW2HzinjSjmsNA2CnQFcZMrNkjVISy0pSd6X2gTBcFo7Whzu2XUdyib3IBFR6qzEXbVdsDlDm3EX60XRUO9TtI6J2ujOPpztChv7r1Y/GxbrXZzhogomNa2deHtuhZIAO4vLYRuhNk789WTpOt4kpQoaDqtyh4k0KkaQmGa6GiL3OhIc7wRejWJo6UrOgsuIjoyI4DRkUBvfOS26tAV2qq97GgDgHFDFdpG6mjTZrQ5vV5npBH/xvY38DWTiEiotTnwTHkDAOAP4/Jg9DB5S8xp29jOQhtRrGGhbRQQ0ZHBKLTlq6cBQ1Foa9E62rybzwb0iY6M0kJNc7cdTrcMSQrcadIx6YlIMOphc7oHRKEEmzcdbUBvcZFz2iJfRXPv7wKHS8Y76yvDuBoiGg1csow/7lZ+11yUl65tXocz36LcOF3XxpuGRMEiDvsFq9BWEMJkDdGt722xSaeTkJYo4iOj8/BRMDraAGBqXujntNW0+dDRps5pK2s8NDpy+IODsdLR1m13aoXmipZuWB3R2ZlJRBRojxyoQY/bjfnmRJyRZfH482alJEICUGVzoM4W3a8RRNQfC20xzu50w67OtUqOC0ahTe1oawvFSVLlBUgMFfeGWGdduzUq53yJjVxGkglGfWD+2ep1EiaK+Mia0MVHuvp05+V6XGhTHlfPQlvEEx1tSXFKVO2ba8ohy6HtmCSi0eWNmiZs7uyB2aDDbePyPPqcOSmJ0EHZ4NZyg0sUFMGOjixMU4rqVSHsaMvwYR+SqRbnmqM0Ar0hWIU2taNtewg72sSMtjwvOtpKMpW/ZwNmtKn7meyUwf9czKKjzeqAO8TpIYHU99+XLA/8c4g0VocLdmf07feJYklTpw23v78FG8pbwr2UoNnZ1YM3a5oBAHdNKIA0QppGX8kGPSYlKfe4NjI+kiimsNAW47rtvVEViUGd0Rb8AkhLt4hs8b6jLTPZBIM650sUeaKJiCYJVGykMEWb0xa6DW5jpw0utwy9TvJ4wy4Kcuxoi3wVLcqF4kWHjUGyyYCypm58t68pzKsioljV6nDiwf01AICbS/KQFefZNUKSQY8pycprC7vaiIIj6B1tanRkU5e9354n0KwOF7rtShdPug/xiSJuUkQwRhOb04UONQI0M9DRkWpH2576jpAURjqsDnSofyc9TdUAgLFadGT/m6HiAGD2CB1tsgzt60ajQ6NZIzk+ssfuwsJHluPER78a0IFIRKHzwMc78fr35fjLF3vCvZSgefpgPdwAzsiyYIGalOENkcCxgfGRRDGFhbYY16VuCuMMuoB1QvUVjhltaT7MaNPrJK0rKhrjI0eKJvHVZLXQtiOEHW3izz87xaTNrBiJ+L5r26KvSDraVKgdbZNyU3DW7HwAwBtrysO5JCKKYQ8fqEWzw4VJSfFYUuDZAHJhnlmNj+ScNqKg6Axyoc2SYERKvPLcwdyLiNhIo15Cig/fi1Zoi8KONlEcNOolrXAUKIVpCTDHG+Bwydhb3xnQ5x6M2IOY4w1edVmWqIW2xk4bOqy9HdAjzc82GfSINyr77/Yojo+sbOl/E3hfCH5WvtpW3Ya6dhsqW3rw0+e+w/6GyF0rUazaWduOd39QIt37zm+PNXu7ldeA83PSfPr8OSlKoe0H7kOIYopHV5jnnnuu10/87LPPIjs72+vPo8ASJ0lFjFugiY62hg4bbE4XTIbgfB2gb0ebb6cp8yzxqGrtUbP5fXsxDJc6raMtwIU29SRpKDvaxHw2T2Mjgd7vu44dbRFPFNqK0hMxJc+Mf64ux2fbatHYaQt45BARjW47OnvwclUjAOBPpQUeDyAX5luS8Gp1E9a3xe5NAKJw6gzinGihIDUBO2s7UNHSgwnZKUH5Gs2dvXsQb6KhhIwkER0ZfQfGxHy2jCSTT9/7cCRJwtR8M77f34ztNe1alGSwiGKs2L96yhxvREZSHJq67Chr7MaMQgvsTrd2CDQ7Zeg9jSXBCKvDhrYeB4p8X3pYiY42g06C0y1jfwR3iu3oM++vrt2GC5/7Hm9ccQQmZCeHcVVEo8sjn+6CmBxR1dIDt1uGzstr9GjQYFcOUOR4mKZxKNHRtrGjG25Zhi7Ar7FEFB4etTi9//77iIuLg8Vi8ejto48+QmcnTw9FgmDPRkhLNGon9WqD3CmmdbT5WmhTN1U1IYi5DLRaraMtsIUK0dFW2dKDdmtoTlrWqvP8cr0oGuay0BYVXG4ZVepNjDHpiZheYMGsQgscLhnvrK8M8+qIKNa8VdsMN4DTMi04Js37G+zz1Y62zZ3dsLs5z4Uo0HqjI4N3EC8Uc9qa1AKZL3OiASBDPWgUjTPaREdbRoBjI4WpeRYASidSsImONm9iIwUtPlKdTybm1hn1EtISh77Jao5XPtYWzR1t6rX93GLloOq+CO4S266mtPx0fhEm56agvkMptu2pC116C9FotrasGct21kOvk6CTALvLHZWjW0YiyzIaHWqscpxv91onJyUgQSeh3enG/p7Y+zMiGq08/o3w5JNPetyh9vbbb/u8IAqsbvUkabAiWyRJQn5qAvY3dKGqtQfFGd5nE3uqRd2cpvsQHQkA+eqmqrot+DGXgVYfpOjI1MQ45FniUdNmxe7aDswvSQ/o8w+mpt37jrZci3KDgjPaIlttuxUOlwyjvjeq9aLDxmBT5Ra8uaYcVx43LuCnoYlo9KqxKTcuD/dhLgIAjE2IQ7pRj2aHC1s7ezDXHLxrGKLRqCvIB/4AJX4QGDhHKpBEgSzDx8N+0TyjTRSUgpVKILrYtlcHP12jRi0Y5XnZ0QYo8ZHrDrZoc7/E4b/slPhhr21F3GZUF9rUf1sLJ2ZhzYFm7KvvhCzLEXlNLzrajinNxK2nTcbPnv8eO2s7cOFz3+PNK4/AxJzgdL0SkVJ8+vMnOwEAP5lfiJV7G1HR3IOKlm6v7v1Egw6XGza30rbn6XzoQxl1EmakJGJNWxc2tHdjQmJs/RkRjVYedbQtX74c6eme34D/5JNPUFBQ4POiKHBER1tikKIjgb5z2oJXBJFlWZtr4Gt0pHhxj8aOtrqO4HS0AX3mtNWG5qRfnQ+nSUXRpsPqDOqwe/JPeZMSv1aYlqjN3/vRrHwkmwwoa+rGd/uawrk8Ioox9errQY7Jtw2uJEnanLZoio+ssNrRyNdCigIdISi0iX1IVRBntDX7uQfJiOIZbVp0ZNA62tRCW007ZJE1FiSioy3fj442UWgThyCzR9ibxUKhrUqd0XbU+AzodRK67K6I7FBxuWXsUvezU/LMSE+Kw5tXHIGpeWY0ddlx+/tbw7xCotj2xY56rD/YApNBh9+dNBGFqUrHeUUMzmmrV2MjU/Q6JOg9uq0+qN45bbH3Z0Q0Wnn0G2HhwoUwGDzfIB1zzDEwmTiLJxKE4iRpvkUU2oK3we1xuGBzKrFOPkdHquusicKuKDGjbbgZAL7S5rTVhGZOm9jketOdl2wyaMXiYEeUku8qWkShrfekcJLJgLNm5wMA3lhTHpZ1EVFsEpvcbB8jW4De+Mh1UTCI3C3LeKysFod9tx2nrNuFLpcr3EsiGpbYh6TEh6KjLXg3qRo7/Su0pWsz2qKv0Ca68LKC1NE2ITsZRr2EDqszqF2JQN/oSB862tTUFjGfTBSackbYm4lCW3uUFtp67C7t7/+4zGSMSVduCu+rj7z4yINNXehxuBBv1GmF0bSkOLxwyXxIErDmQLN2KJCIAsvllvHIZ0o326VHj0WuJR5F6crv2orm6EuUGkmDeuDN1242Qcxpi6ZCW6PdCYc7uAdjiKKZ16X3hQsX4tVXX0VPT+z9soxFovsnWNGRQO9A6WAW2sTGNM6gQ5KP3Xn5qaKjLbr+7jpdbu00aaCjI4Hejradoepoa/d+kytJUp85bZF3gpIU4rSa2IQLFx02BgDw2bZa7e8yEZG/6myi0Ob7JneeRfl9ta4tsgttzQ4nfrF5Px4+UAsZQJXNgZcqG8O9LKJhaQf+/CiGj6RALbQFc0ZbszqjzdfoSDGjrSkKr4EagxwdGWfQaXF+24N86E+MD8hL9X4/VZKpvFaUNfWPjhwpbcQc5R1tVa3KtX2KyQBzggHjs5QCViTOaduhzmeblJOiJWsAyr2KYyZkAgDe2cCZ0UTB8O6GSuyu64Q53oCrFo4HABSpM1QrgngQJlx6C23+Xd+IQtu2zh7Ywjwv+t26Fpz/w168UNmAdufAw3wb2rrwi837Mf3brfjtjoNhWCFRdPC60DZnzhzcdNNNyM3NxRVXXIHvv/8+GOuiAOlUZ7QlBnGDKwpYwYxsaelSNifpiXE+58GLwk5Dpw12Z3hfxLzR2GmHLAN6neTzBn84U9SOtl21HXAH+WSKLMvaadJcL4uGOVqhjR1tkapcLbQVHVJom15gwaxCCxwuGe+s5waXiPzX7XKjw6W8lvsaHQkokS06KIWrWltk3gjd2N6Nxet24cvmDsTrJJyXkwYA+Gt5PToG2QgTRYrOkMxoU6456jtssAXp34MWHeljfKK4fm+3OqNqDwIEPzoS6I2P3BbEOW2yLGvjA/L96Ghr7XagtdvemzYywn4m2qMjRZdhQVoCJEnCuKxkAMC+hsg7nCLms4m9bV/nzysEoBTagr3fJRpt7E43nvhiDwDg6hMmwJKo/N4T9wRiOToy08/7rGPilXnRDlnGts7wNgQ8tL8GK1s7cfueKsxetQ237KrAjs4efN/aiZ9u3IfTN+zBF03K79n/1rdic0fs/VyJAsHrQtsTTzyB6upqvPTSS6ivr8dxxx2HqVOn4v/+7/9QV1cXjDWSH8RJ0mRTKGa0BfEkabeywfU1NhJQNrlxeh1kObqKNb3Dtk3Q6QI/dHpsZhKMegmdNmdQi6WAsskUEaAjzTQ4lJixVxtFP7vRZqiONqC3q+3NNeXc4BKR3xrUDW6CTkKKH7MRkgx6TElWXl/C3dW2qaMbF23ahws27sXPNu3Dki37cfnWA/jxhj2otDpQkhCHj+ZNxJNTxqA00YQWpwt/r2gI65qJhtNpC36yRlqiEQlGZZ8TrOhBMVstI8m3ri5LglHrsGnpjq74SBEdGayONgCYmq/OaQtioa2tx4Eeh1KIzfVhRluSyaB1rx1o7NKiI7NTYntGm/g3JSJaI7ujbehC2ynTcpFiMqCypQerDzSHemlEMe3bfY2oau1BZnIclhxVor1fREcGOxY4HMSsZH9SNQAluWlOivJ7dUMY4yNrbQ4ctNqhAzAxMR7dLjderW7CCWt34ewf9uLrlg4YJODC3HQsylB+x/7lIO//Ew3GpzsTBoMB5557Lv773/+isrISP/vZz3DHHXegqKgIZ599Nr788stAr5N81GUPwYw2rdBmDdoQ6xZtCLnvL2Q6nYQci7IZqomiOV9aoS0IsZEAYNTrMCFbiWzZEeTIFvHnnp4Uh3ijd8VfUZjjjLbIVa7mr4uYiL5+NCsfySYDypq68f3+plAvjWjUsTpc+HZvI2raYm9zC/TGRmbFGX3udBfmRcictqcP1mN5cwe+aenEl80d+LSxHR81tMEuyzgt04LP5k3EtOQE6CUJN4/NAwD8vaIezQ5nWNdNNJQuNVkjOYgz2iRJwjS1UPPm6uDMghUdbb52del0EtLUE/6icBUtgh0dCfR2tAVzH1Ld6vseRBBdbWVNXahv92zmdOwU2pRr+/FqR9v+KOtoizfqceYs5XWT8ZFEgfXJlhoAwGnT8/r9fhX3BGraeuBwRVc390jEgT9/oyOB3vjIjWqhzeGW8UpVIxZ8tx0XbtwXtHusfa1uUw5PTEtOwNeHTcI7s8fjzCwL9BJglCT8Mj8Dqw6fgiemjMEfxym/Sz9uaMPuLt6bIzqU70eAAaxZswZ33XUXHn30UWRnZ+O2225DZmYmzjzzTNx0002BWiP5oSsEkS3iVGCPw4XW7uBsIsQGNy3Rv9gSER8ZTTce67Rh28Hb4E4J0Zy2Wh9jI/t+Tn0HX8wjkTIsXfm7OlhHW5LJgLNm5wMA3lgTnBthRKNdj92FT7fW4Lo3f8C8+z7Hz19YjfP+tkrrKokl9epBohw/T5ICwHyLcvN0fVv4TpLKsoy1akfdLWNz8cTkIvzfpCI8UFqAl6ePxT+ml8Bi7L2WOzPLgmnJ8ehwufG38vpwLZtoSLIs9znwF7xkDQD47YkTAACvfncwKOkMzZ3iwJ/v+xDxuWJPEw1cbllbb2YQoyOnqIXSqtYetAap40/s/fJ86GYTxmYqrxUHGvp0tHk4o609agttyuui6GgT0ZFVrT3osUdOdHFrtx3V6j5zcl7KoI8R8ZEfb6nR7pEQkX8cLjeWblc6m06bkdvvY1kpJpgMOrhlaNG9saI+QDPagN5C24b2bnxY34qFa3bi1t2VqLDa8VVLR0g63da0KnuQwyxJkCQJR6el4IXpY7H16OnYcvQ0PDypCGMSlNe7KckJOC3TAhnAk+xqIxrA60JbfX09Hn30UUyfPh3HHnssGhoa8Oabb6KsrAz33HMPXnjhBSxduhTPPvtsMNZLXupSL4CT4oK3wY036rVTjsGKHtRmI/g5oyxf3VxFU0ebpycm/SE2JDtrg9vRJmIffYlsEYU2drRFJjHkOCXeoOWyH0rER362rVYryhFRYDy6dBfm3vc5fvP6BnywqRpddhckCahus+LRpbvCvbyAq1NPkmYH4CDRfLWjbXNnN+xuN1odTnxY34qbd1Xgnr1VcIXgJGmVzYFauwMGCfhNUTYuzMvAL/IzcFlhFk7Nsgzo2tNJEm5Vu9perGxAfYTOl6PRq9vugvinE8zoSABYODELR47LgN3lxuOf7w7oc9ucLnSoN+X9mZUsYiebuqLn+qel2w63DEiS/3uw4ZjjjVrE2PYgdbWJIkyeD/PZhBK10La7rlPbm+akxHZHm9jbi1ER6UlxWnfm/sbIiY/cUaMcFi1MS4A5fvB9yNwxaRibmYRuuwufbK0N5fKIYtbq/c1o7XYgIykOh5Wk9/uYJEkoUIv04l5BrGgIUHQk0Fto299jwxXbyrC/x4YMowHT1Gj7d+ta/P4aI1mjHvY7LDWp3/vTjAakGgdew/2uOAcA8F59Cw72RM91DVEoeF1oKywsxAsvvIBLLrkElZWVePvtt3Hqqaf2uwEwc+ZMLFiwIKALJd+I01qJQd7gFqQqLwJBK7R1B6ijTd0k1AR5Flkg1WmFtuB1tE3OVU6S7qwJbkebKHD6UmjLUT9HDB+nyDLcfDZheoEFswotcLhkvLOesS1EgdLYacNfl+9Fj8OFwrQEXHncOLx39VF4+dLDAACvrCrD5srW8C4ywOoDuMEdm6AMIre5ZZy8bjemrtyKK7aV4bXqJjxT0YBlTcE9hAJA62abnpyIRA9nzp2cYcZccyJ63DKeKueJUoosopNWJ0GboRYskiTh1tMmAwDe3VCJ3XWBu55t6VIKJHqdNOQNfE+kqx1h0RQdKQ5FpSXGweDHLExPTMuzAAC2VQXn963Y++Wn+t/RtrZMmfEVp9chdYjDZUK0F9oOjY4EervaIik+crjYSEGSJJw3twAAuA8hCpCPtyqxkYun5Q76OiHiI8W9glihRUcOUoTyVrrRgAmJyr2+RL0ON5bkYPURU3DbOCUN6L/1rXAGccZ9h9OFbZ3K7/rDLckefc5scyKOT0uBSwb+ymQNon68vmJetmwZduzYgZtvvhlZWVmDPsZsNmP58uV+L4781xWCIeRAb177p0E6HdYS4I626ijqihKFpWDNaAN6O9oONHUFNQakTpwm9eF7yekTHekO4oUG+aZcvXgebD5bX6Kr7c015fw5EgXIsh11cMvA9AIzvrnlBPzh9CmYMyYNCydm4azZ+XDLwG3vboEzhuYjiBltOQGIbJEkCQvU+MhdXVa4oQwCn5WiHM75T23wT5KKQtsCy/C/Q/uSJAm/V7vaXqlqQpU1em7gU+zr7BNf7+8cRU/MLkrFadNz4ZaBhz8NXBdv32KTTuf795ERhdGRjR3qbLogdrMJc8akAlCuD+3OwL9W1QSgo00U2prUn2FWimnEv9ui0NZudYZkzk4gWR0uNKgRmSI6EgDGZyl/DvsaIqmjbeRCGwCcM7cQkgR8t78p5m78E4Wayy3jM/X+32nTcwd9jOhWjqWONlmW0ajOR84MwD4EAJ6aUow7xufj+8On4JaxeUg26LEwLQXpRj0aHU6sbA3egfh1bV1wAyiOj0OuyfMDRb8rUbra/lXTjBpb9FzbEAWb14W2Y489NhjroCARQ8iDOaMNAC49eiwA4P2NVdhbH/gXAW1Gm58bvdxonNEWgujIrGQTMpLiIMsI6CngQ9WI78WHjrbsFBMkCXC4ZK3DkSJHRbPyb2pMxvA3iX80Kx/JJgPKmrrx/f6mUCyNKOYt3aZ0M50yNXfATb/bz5gKc7wB26rb8fKqsjCsLjjqtehI/zvaAOAP4/JxRWEmHp9chA1HTsWKwyfj/yYVAQCWNrWh1RHceS69hTbPTpIKx6Yl48jUJNhlGc9U8EQpRY5QHfbr66ZTJkGvk/DFjjqt68gfPXYX7v1wOwCgeITrm5GIw4JNUVRoEzGXYkRAMP3s8DHITDZhf2MXXgnCa5XY+/nT0TYmPRF9X2I9SRsRhTaXW9ZGOkQLkVSTFKfv17k3PhI72tTxB1OHmM8mFKQm4KjxGQCA936oCvq6iGLZmgPNaOqyw5JgxJHqv6tD9Xa0Rc/9t5G0O12wqQeGswKQrAEo8ZHXjMnut68x6iT8KCsVQHDjI4eKjRzJkanJONyi7EGeLW8IxtKIopJHhba5c+eipcXzf9jHHHMMqqp44RIJtCHkQZzRBgAzCi1YPDUHsgw8/sWegD9/S3dgTlSK03gHm7qjpptGDNsOZnSkJEkhmdOmdbT5UGgz6nXafAvOaYs8vR1tw58UTjIZcNZsJQbhjTXlQV8XUazrsjnxzd5GAEpsy6GyUkz4w+lTAACPfb47aBHPoRbI6EgAmJQUj/tKC3FRXgby45VrjenJCZiSFA+bW8aHDa0B+TqD6eoT2TLf7N3NfEmS8OvCbADAl03BjX8m8kbfjrZQGZ+VjJ/MLwQAPPTJTr86iKwOF658bR3WlDUjxWTA3T+a5tfaMtRiVXMUzWgT3UyZKcEvtKXEG3HLqZMAAE8u26N97UCpbvW/oy3eqEd+n8/PHmE+m/I5OsSpcWrRFh9ZpcZGFqQl9DvEI6IjI6WjzelyY3edspaROtoA4Px5yu+It9dXRl2XIVEk+UTERk7NgXGIeOEidaxELHW0NaiH71L0OiQEOVb53Jw0AMDHDW3oCVIyyWq10OZpbGRfYlbbq9VNaLIH91AiUbTw6LfCxo0bsWnTJmzevNmjt40bN8Jmi55NRCzrCuEm94aTJwIAPtpco8U3BEqzOh/B3xltpdnJSIzTo8PqxJ76yNgcDMfmdHk8bNtfYk7bjiDOaROnSXN97M7LtSgbfdHlR5GjUr14LhpmRpsg4iM/21arRTIRkW9W7G6A3elGcUYiJuYMvkH6yfwiLChJQ7fdhbv+uzUmbizV2QMXHTkUSZJwQa4y2P2tmuCdJP2hoxtuAAUmo1bk88ZRacnQQRmizugWihShStU41O9OmgiTQYd1B1uwbIdvXZ4Olxu/fWMDvtnTiMQ4PV6+bAFmFFr8Wpc4LBhdM9pCFx0JAOfPLcTMQgs6bE7832eBi/9s7rJrB8ImZHt/I7Gvksze61xPDkFKkgSzmNPWHV2FtsHmswG90ZH7G7oi4uDq/sYu2J1uJMXpR4ywB4BTpuUi2WRAeXM31pYFPxqaKBa53bI2Nub0GXlDPk78mxS/T2JBg1pQClQ323AWWJJQYDKi0+XGF0GYGW13u7GhXRTavOtoA4AT0lMwMyUBPW43/lXrf5IAUSzwuPx+0kknYfbs2R699fTEzi/RaCc2uaGIbZmSZ8YZM5UX2Se+2B2w55VlWeto83dGm0Gv02YABCJSJtjEaU5Phm37S5wAXLGnIShzfLrtTrRblYuSXB862oDeYqOYW0eRQZZl7QbGGA8KbdMLLJhVaIHDJePdDRxGTuSPpduV2MjFU3OGnBWj00l44JwZMOolfLGjHp+rnxOtXLKsnZrMCfIm99ycNOgArG3vwoHu4Lz2aJEtPmxwAcBs0GOGOk9uVUvkHyKi0aFTnaOYEuJCW64lXou0v+uDbdhe7d2NKZdbxvX/3ogvdtQjzqDDC7+cj3nF6X6vKz0KZ7Q1qYehskLQ0QYor1V3qZ2Db62vwJbKtoA8r9jzTcxJ9nsvWZLR+3va0/nZlgTl30C0dbSJQ3SFh6RVFKUnwqiX0ONwoTYCDj+KA76T88wezVFMjDNg8VSlC2PFbsadEfliQ3kL6jtsSIk34KgJg8dGAr0z2ho6bLA6ois+dygivj4riIf9BJ0k4Ry1q+29IMRHbunogdUtI92ox4RE71/rJUnC+er6VrdyD0IEeFhoO3DgAPbv348DBw549LZ//34UFxcHe+00ApdbRo/6YpYY5OhI4fqTSiFJwGfb6rC1KjCbo2/3NsGlnpYLRLFJbJbXH4z8E2yioJRtHnnYtr9OnpqD1EQj9jd04T/rA1/8EHGPSXF6pMT79nMUs90iYVNHvZq77Oi2uyBJSryMJ86aXQAA+G4f57QR+crhcmPZDrXQNkhsZF+lOSn45ZElAKCdQI1WjXYn3FAuYjOCvMnNNRmxMF2JVv5PXXAO6Ij5bPN9LLQBwFGpSpfGKm5yPVZVVYVf/OIXyMjIQEJCAmbMmIF169ZpH5dlGXfeeSfy8vKQkJCARYsWYc+ewMejx6pOraMtNHuQvq5aOB6FaQmoau3B2X/7Fq9/f9CjTt6ath789o0N+GhzDYx6CX//xTwcNSEzIGvKiMIZbSJ1IDM5NB1tADCvOA3nzCmALAN3f7gtIB3Yaw4ov7sPG+t/wXRsZp9Cm4cFSDGnLfoKbaKjrf+1vVGv0w7WRUJ85Ha10DZlhPlsfc0rUW4Mb6psDcaSiGLex1uUvcTJU3JgMgz9Om9JMGqH/itjJD6yt6MtNAeJRHzksuZ2tAV4ZvTqPof9fL3fKPYv69q7YiI1hchfHhXaiouLvX7T60O/qaL+uvpk5IYqtqU0JwVnzVLmLz32uf9dbVsq2/Dr15SbHufMKUC80f+/VwvUC+to6GirVwtKOT5GLXrDkmDEdSeWAgAeXbpbix0NFFFo87WbDeiNnKzjjLaIIrrZcs3xw15o9zWrSIlg2ublSXMi6rXmQDParU5kJMVh7pi0ER8/qygVQPTPSRCxkZlxBuiDfAgFAH6ixkf+p7YF7gBvIN2yjPXt/nW0Ab2Ftm9ZaPNIS0sLjj76aBiNRnzyySfYvn07Hn30UaSl9f47evjhh/Hkk0/i2WefxerVq5GUlIRTTjkFViuvQTwRyvj6Q1kSjfjgt8fgxMnZsDvduP39rfjtmz+gwzp4oaOly44HPt6B4x/5Cp9srYVeJ+Gpi+bghMnZAVuTmNHW1uOAI0hzVgKtNzoyNB1twq2nTkZinB7rD7bgg03Vfj9fb6Ft6K4LT/UttHm6PxPRke1RVmgTM10PjY4ElHmIALAvAkYxiLEHnsxnE2YVpgIANlW0RkT8JVE0cbtlbT7bqdOHP+gnSZJWrK9ojo3ks8YQRkcCwJSkeExSZ0Z/3Dh8M4NblnHfvmpcuHEfni2vx/4R0jjWtCm/ww/zYT6bMD05AfE6Cc0OF/b3MHmKKLiTGymsutWTpHqdBJMhdD/q3y2aCL1Owpc767Gh3Peusf0NnVjy0hp02V04clwGHjx3RkDWN2dMGnSSckpPzAyLVHVaoS00G9xfHFGM4oxENHba8PcV+wP63KILzZ8h5KLQxo62yFKhnnj1ZC6CMDnXDEkC6jtsqO/gz5PIF0u3KadJF03Jgd6DuKSiGNno1ocoNlI4JdOCZL0OFVa7dvIzUHZ1WdHudCNRr8OUJN9fHw9PVea0lfXYUWWNno6ZcHnooYdQVFSEl156CYcddhjGjh2LxYsXY/z48QCUbrYnnngCt99+O8466yzMnDkTr776Kqqrq/H+++8P+bw2mw3t7e393kYrUWgLRXz9YNKT4vDCL+fjD6dPhkEn4aPNNTjzqZV4dOkuPL9iP95aW4FPt9bgyWV7cNzDy/Hciv2wOd1YUJKGt359JE6dPvTMGV+kJhghfk23RElXm4iOzAxRdKSQa4nHNSdMAAA8+PFOdNt9P/zXYXVgW7VyY/KwEv872kp8KLRFb0ebciinIHXga9M4tdC2vzGwr4m+ENGRU70otE3KTYHJoEO71YmypvB/D0TRZFNlK2rarEiK0+O4iVkjPl7McI/2g35CKKMjAaVYeW72yPGRsizj7r3V+Gt5Pb5q6cDd+6px1OodOHb1Dty7t3pABL5blrX4+iP8OOwXp9NhVoryM14b4H0SUTRioS2GdYqTpHH6oMcO9jU2MwnnzlFi4R5dusunU2K1bVZc/OIaNHXZMb3AjOd+OS8g3WyAsuGfmq9ciK+L8AHItSI6MiX4HW0AEGfQ4fenTgYAPL9iv1boC4SaNv+787LVgmMg10X+q1A72oo8mM8mJJkMGKferGBXG5H3ZFnunc82LcejzxH/Rus6rLA5o3dOQr0ttBvcRL0OP8pOBQD8J8CDvtep3WxzUxJh8KBYOhSzQY+Z6ib3O3a1jeiDDz7A/PnzccEFFyA7Oxtz5szB888/r338wIEDqK2txaJFi7T3WSwWHH744fjuu++GfN4HH3wQFotFeysqKgrq9xHJOqzhLbQBysyvK48bj7d+cyQKUhNwsKkbT325F3/6eAdueWczfvP6Bjz2+W502JyYkmfGS0sW4K1fH4l5xSN3CPuylrTE6ImPlGVZ62gLZXSkcPkxY1GUnoDadiv+ssz3yNb1B1vgloHijES/UjWEorREJJsMiNPrkJ8au4U2m9OljTA4NDoSAMZnKdfw4Y6ObOy0oaHDBklSimeeMup1mKbeD2B8JJF3PlEj6E+ckuPRPTpxGFfcM4h2IjoyO0QH/gDg7JxUAMDKlk7U2QZ/LXnyYD2eq1TmTl5RmIlj05JhkIA93Tb8raIep63f3a/DbW+3Dc0OFxJ0Eqan+H7YD+iNj1zfHhs/YyJ/hL3QxvkIwSNO/4Vjg3vdSaUw6CR8u7cJV7623quNRWu3Hb/8x2pUtfagJCMRL196mM8zvYYyP0rmtIUyOlI4dXou5hWnocfhwmNL/Y//FER0ZJ4/0ZHq57LQFlnKm0ShzbsLtGn5SnzkdhbaiLy2taodNW1WJMbpcbSHM4QykuKQYNRDloHq1uj9PSqiI3NModvgXpCjXDd8UN+KngDGvomTnwv8OEkqMD7Sc/v378czzzyD0tJSfPbZZ7jqqqtw3XXX4ZVXXgEA1NYqN5FycvoXsXNycrSPDea2225DW1ub9lZRURG8byLChTM68lBzx6Th4+uOxS2nTsIlRxbj7Nn5OHFyNuYVp+GwknQ8edEcfHTtMThhcnZQDyemq3PamqOg0NZudcKu/q7LTA5tRxsAxBv1uPtH0wAAL35zADtrfbtWXC1iIwPQzQYohxL/+avD8drlnu9Po7HQJq4REox67e9tX+Oz1Y62hvB2L4hutrEZSUj08vCNiNPeVBGYufJEo8XXu5RizqkjzIcWxD2CaE/UEEI9ow0AihNMmG9OhBvA3ysaYHf334u8UtWIBw8ocZ73TSjAfaWF+M/sCdh+zAz8fVoxZiQnoNXpwsWb96NVnfMmutnmmJMQp/OvNLDArOxj2NFGFOZCG+cjBJfoaEsMwwa3KD0RD58/E3EGHb7YUYeznl454gZJlmV8vKUGP376W+yu60R2igmvXX54UDZ386NkTltdR2ijIwGlNf0Pp08BALy1vkLbwPhLxHTmBGBGW0u3A1ZH9HZjxBoRAzHGi442ANpJUhHpQ0SeW7pdudm/cGKWxx3f/eckRO+Jw1BHRwLAEalJKIqPQ6fLjc9GmI/gjYAW2tKUG5+rWlhoG4nb7cbcuXPxwAMPYM6cObjyyitxxRVX4Nlnn/XreU0mE8xmc7+30aorjAf+BmNJNOLq4yfgnrOm44kL5+AfSxbgnauOwlu/ORI/npUPnR8dpZ7KSI6ejjYRG5lsMgQsVcRbJ03JwSnTcuB0y7j9va0+paT0zmcLTKENUAo0h4/zfN6bKLS1DzEjMBJVtYj5bAmDFp/HZyqvNzVtVu2eQziIfao389mE2aLQxo42Io/JsoyDzcq16/QCz/7daR1tMRId2SCiI42hvb45X50Z/beKesxZtR337atGWY8N/61vwe93VwIAbijOwRVFvXGeZoMeZ2Wn4Z8zx6HAZMS+Hht+tbUMDreM1ep8tsMDsAeZZ1F+xru6rGhzhO81gSgSeF1oGzduHJqamga8v7W1FePGjfPquYI1H4EUXeqMtnCdJD13biHeVqNaypq6cfZfv8V/N1YN+tj1B1tw3jOrcPU/N6C8uRvZKSa8evlhXkXReUN0tO2oaR9yMHokEJEdoexoA4B5xWk4Y0YeZBl48JOdfj+fyy1jndo9OMWLWI9DWRKM2rzBhg4OWo0U5T5ERwK9HW2MjiTy3tJt3sVGCrEwJyHUsxEAQCdJOD9HOaTzTEW9Ngh9MHa3GzW2kW+kN9gdONBjhwRgntn/653DLUnQS8BBqx2VnNM2rLy8PEydOrXf+6ZMmYLy8nIAQG6uckq7rq6u32Pq6uq0j9HwOsO8D4lEGUnKwbnmzsi/hg1nbGRfd/94GpLi9Fh3sAX/We9dh2iP3YXNahHl8LGeF8YCzRyFHW3afLZBYiMBpXAt/m7srQ/f4Y4dNR0AgCl53u8vZxWmAlD2IXZn4DrViWJZU5cdVocbkgTkWTxLsxF7j8qW6O9ok2W5t6MthMkaAHBxfgZuHZuL3DgjmhxO/LW8Hkd8vwNXbz8IGcAv8zNwy9jBr1GzTUa8NnMckvQ6rGztxB/2VGJ1q1IwPTzV/0JbVpwRJQlxkAFsYHwkjXJeF9rKysrgcg3sJLHZbKiqGryIMpRgzEfgEPJevdGR4TmFCAAzC1Px4bXH4NjSTFgdbvzuXxtx+l++wS9eWI1r/rkBf3hvC654dR3Oe2YVNpS3IsGox+9OKsXym47H5NzgnQLOtcSjKD0Bbhn4obw1aF/HX3Xtoe9oE245dRKMegkrdjfg690Nfj3Xlqo2tHY7kBJv0E4P+kKSJK3oWDvK4iM3VbTip3//Dl9srxv5wSHkcLm1+Xu+drQdbOqOqlO+ROFW1tiFXXUd0OsknDjJy0JbWvTHt4jZBKHsaAOAC/PSkaCTsKmjBwvX7MTHDa39Pu50y3ijpglHfr8Dc1Ztx3U7Dg5bkFundrNNSoqHJQCnYlMMesxM5pw2Txx99NHYtWtXv/ft3r0bxcXFAICxY8ciNzcXy5Yt0z7e3t6O1atX48gjjwzpWqNVp/q6HikdbZFARPBFQ0dbo1oMzAhDbGRfeZYE3HDyRADK4b8mL4qUP1S0wOGSkWuO9zrePJCiMTqysk9H21CmqgfmtlaFL5nCn4624oxEWBKMsDvd2FXbEeilEcUk8bsh1xyPOINnt5PF75G2HkfU7/nbnS7YZaW7OtQdbXpJwg0luVh35FS8NL0EJ6SnQALgkoEfZ6fiwYmFw8ZfT01OwDNTiyEBeK26CeVWO3QA5pn9L7QBwHz1ecT8aaLRyuNC2wcffIAPPvgAAPDZZ59p//3BBx/gvffew3333YeSkhKvvngw5iNwCHkvLToyhCe+B5OeFIeXLz0Mvz1hAgBge007Vu5txEdbavDG6nJ8vr0OkgT8dH4Rvrr5eNxw8sSQnH4VXW3rInROW7fdqQ2Szw5xRxsAFGck4ZdHlgAA7v1wm18n/USO9zETMmHQ+5dYK+Ijxcy30WBPXQcueWkNVh9oxgOf7IAsex+dEyw1rVa43DLiDDpkeXkzJi0pDgWpyoU357QRee5zteB+xLh0WBK9KzbFRkebGh0Z4pOkxQkmfDC3FFOS4tHkcOKyrWX47faDaHE48UF9K45fuxM37qxAlVoIfKu2Bces3oFXqxrhHuT39to25WcQiNhIQcRHfsv4yGHdcMMN+P777/HAAw9g7969eOONN/Dcc8/hmmuuAaAc7Ln++utx//3344MPPsCWLVvwy1/+Evn5+Tj77LPDu/goIZI1WGjrFU2FNlHQCndHGwAsOaoEU/LMaO124IGPPU/a6BsbGczZeyOJzkKb8vpUmDb0IbqZBUqhbUtleAptTpcb+xuVG7oTc7zvaJMkSZvTtpHxkUQeEdHzwxXhD5VkMmivf9EcXQ8ADWosYopeh3g/72v5yqCTcFpWKt6cNR6rj5iCl6aX4K9TiqH34HVucaYFd43P1/57WnICUgyBacyYr+5n1rVF98+YyF8e73zEplKSJFxyySX9PmY0GlFSUoJHH33Uqy/udrsxf/58PPDAAwCAOXPmYOvWrXj22WcHfA1P3Xbbbbjxxhu1/25vbx+1xTYxhDwSNrh6nYSbTpmEc+cWoKypC209DrR1O9Da44DD5caZM/N9Oonmj/klaXjvhyqsi9A5bfVqbGSCUY+UMP0MrzupFO//UIV9DV14ZVUZrjjOu3hYYcUepdC2cGLWCI8cmZjxVjdKOtoqW7px8Ytr0NqtbM73N3Rh3cEWLAjQUHd/iZv1RWkJPs03mZpvRlVrD7ZVt+MIL+ZdEI1ma9TXrRMmZXv9ueKmWbTGt8iyrEVHZofhINGMlER8On8iHj1Qi6fL6/F2XQs+qG/VTremG/W4bkwOZqYk4o69ldjWacUtuyvxr9pm3DU+H/MtSdpGWJz4nB+gk6QAcHRqMv5aXo9V7Ggb1oIFC/Dee+/htttuw7333ouxY8fiiSeewM9//nPtMbfccgu6urpw5ZVXorW1Fccccww+/fRTxMeH/vBTNBIH/pLCmKwRaUTRqjEK4s8btOjI8Ha0AYBBr8MD50zHuc+swjsbKnH+vEIcOX7ka8ZgzGfzhTlendEWVYW2kTvaZhQqhbbNYepoq2jpgd3pRrxRpx3c89bsQgtW7G7ApopWXHxEcYBXSBR7en83eJdkU5SWgOYuOyqae7TxEdGoXr22yQ5xqsZQxiSYMCbBu9fpXxdlYW+3Da/XNGFRRuDuwYqDg+vbu+CSZY8Kf0SxyOM7FG630s0yduxYrF27FpmZmX5/8aHmI7zzzjsA+s9HyMvL0x5TV1eH2bNnD/qcJpMJJlP4NwSRoHdGW+RscMdlJWNcVnK4lwEAWqHih/JWOFxuGMN0ImUoopCUa4kP2ylMS4IRt546Gbe8sxl/WbYHZ83O97q7rq3bgR/Kla7B4wJQaMtVYzRHQ6GtsdOGX764BrXtVkzITsaErGR8uq0W/15bETGFNl/nswnT8s34fHsdtlWHL3aGKNqITe54H15PxU2zyig9UdrudMHqVopa4drkmnQ6/GF8Pk7NtOC6neXY221Dkl6H3xRl4TdF2drJ0M/mTcI/qhrw0IFabGjvxlk/7EWaQY+j05JxbFoKNnUoP4PDAtjRdpg6p63cakeF1Y6i+PB3o0SqM888E2eeeeaQH5ckCffeey/uvffeEK4qdnTZI+fAX6QoVbtuvtnTiKZOW9hjGYcTKdGRwpwxafjZYWPwz9Xl+OP7W/DRtcciIW7oPa7d6cYGdf9xeJgLbaLzvK3HAVmWw9pd56mqVuU6Y7gC1ky10La7rgNWhwvxxtDecxCz4cZlJvt02A+A1tG2qaI1QKsiim293a7eFbcL0xOxqbJN+/xo1eAI/ZzoQJMkCY9MKsQv8jMwJTlwh8cmJ8UjSa9Dp8uNXV1WTE0OX2QzUTh5XVk4cOCAVmSzWv270c35CMElOtqSovhFIJgmZCXDkmBEj8MVkbF1depp1+yU8G5wz59XiFmFFnTanHjo010jf8IhVu5thFsGSrOTke/jacO+eme0Rf5pYH90WB1Y8tIa7G/sQkFqAl67/DBccdxYAMBHm2vQESH55iL+wdv5bMJ09URbJP4bJIpU1eoNMF9+p4qieFOXXbtOiCYiNtJs0CEhzAdk5lqS8Pn8SXhpeglWHzEVN4/N6xe/YtBJuLIoGysPn4zzc9KQrNehxenC/xracOvuStjcMjKMBpQkBK4YlmzQY1aK8jNexfhIChNZltGpxp8nx3MfIhw+Nh0zCy3ocbjwwsoD4V7OsER0ZFYEREcKt5wyGVkpJuxv6MK9/9s+7GO3VLXB6nAjPSkOE7LDe8hTREc6XDJ6HANn3Ucau9OtzcIermsl1xyPzGQTXG4Z22tCfx0vCm2lOb7/fGcWpirP1dAZMXur0cbmdOHTrbVoiYJIXfKs23UwRervkqiPjlT3IZlRfo9VkiTMNifCpAvcXkovSZhrVn7OYg410Wjk9b8qt9uN++67DwUFBUhOTsb+/fsBAHfccQdefPFFr56L8xGCS5wkDcW8s2ik00mYV5wGIDLntNWrG5ycMMxn60unk3D3j6cBAN7ZUIn1Xv5ZrditxEYGopsN6P3zqIvhGW1ut4wrX12PrVXtyEiKw2uXH4Y8SwLmjknDhOxk9Dhc+HBTTbiXCQA4KDravIyPEKYVKHEFe+o7YY2Cmw9E4dZpc2pzXvJTvX99sCQYYVZvfEdjfGSdFhsZGZEtCXodTstKHXbDnWeKw9NTi7HjmBn4cG4pbi7JxRGWJMTrJPwsL/Czg45OVW46Mj6SwsXmdMOpdp5yH9JLkiRce2IpAODVVWURfWO5UY2OjJSONkDpDHv8J7MhScCba8rxv83VQz5WxEYuKEkLewdZUpweerXjKhrmtNW09UCWAZNBN+yMPkmStK62cMxp21PfAUA5POurrBQTClITIMtKcZZC799rK/Cb19fjlCdWYNXexnAvh0bgyfzGwRSlK4W5iijce/QlCm1ZEbIPiTQiDn9tOwttNHp5XWi7//778fLLL+Phhx9GXFzvhdf06dPxwgsvePVcYj7Cm2++ienTp+O+++4bdD7CtddeiyuvvBILFixAZ2cn5yN4SERHJg4TqzHazS9RC20ROKetTiu0hX+DO2dMGi6YVwgAuPuDbXCpN09GIssyvt4duPlsgBKlCUA7aRmLfqhowXf7m5Bg1OOVyw7T4lYlScJP5yszJ/+9tjycS9Tsb1AuosZl+RZ9lmuOR3pSHFxuGbtqOwK5NKKYJLrZzPEGpMT7tskTXW3RGN8iOtoipdDmDaNOwgJLEv7f2Fy8P7cUB46biT/2GUgeKEex0EZh1rdblska/S2ako0peWZ02V34x7eR29XWrBYBM5Iip6MNAI4pzcTVx48HANz2zpYhuyPWHGgCABw2NvzzfyVJ0rraoqHQ1rdjZaQi5YwCdU5bGApt+9SONn87FmcVhe97IGBbldINWd9hw89fXI1HPtsJh8sd5lXRYGRZ9rujLRr3Hn01hHFOdDQQc9rY0UajmdeFtldffRXPPfccfv7zn0Ov7y3gzJo1Czt37vR6AWeeeSa2bNkCq9WKHTt24Iorruj3cTEfoba2FlarFV988QUmTpzo9dcZjcQml7MRhibmXK0ta4Ese1Y8CpW6dhEdGRlF5VtOnYwUkwFbqtrw1roKjz5nT30natutMBl0ARtEnis62tqtEfczC5S1ZUrX4LGlmZhe0H9Y8DlzC2DUS9hU2YYdYYhp6cvtlnGgUZ2P4ONpUkmSMC1f6WrbxvhIohFVqRvcAh+7SIHojm+psykb3JwY2OAGq8tCzGmrsNpR3hPbMcsUmcRhvwRjbycPKSRJwu9OmgAAePnbsogtvLR2K4W2tAgrtAHA9YsmYl5xGjpsTlz75g8Dbsq73DLWlUXGfDZBFNraeyI/snmjOq/Mk2t7raOtqjWIKxpIlmXsUw/7+RMdCQCz1PhIzmkLj/3qXnJavhmyDPx1+T789O/fReU1aqxr7LTD5nRDkoA8i5cz2tTCXEVzT1Tfw2FH2/BEdOSBHrtWlCQabbwutFVVVWHChAkD3u92u+Fw8B9SJGF05MhmFFgQp9ehsdOG8gi7mGtRN7jpEbLBzUox4fqTlSL3I5/t0k66DufrXUo32xHjMgI2IDtLnVlnc7oj9uaEv8TNAVEI7isz2YRFU3IAKFEb4VTd1gOrww2jXkKRl6fa+pqmzmnbVs2TpEQjqVQ72gr8mHmpbXajML6lXpwkNXGDO5Qkgx6z1TltKzmnjcKgQy2Icz7b4BZPzcWknBR02Jx4+duycC9nAFmW0a7O2BMFokhi1OvwlwtnwxxvwMaKVjy6dHe/j++oaUeHzYlkkwFT8sxhWmV/5ijqaPtqVz0Az2L/RUfb3vrOkM59rW23otPmhEEnoTjDt1QNYVZRKgAW2sLlQKNSMP3zuTPx15/NRUq8ARvKW3HGk99EffdTrBE/j1xzPOIM3t1KLkhLgCQBPQ4XmiI4NnkkYh+SFQMH/oIh1WjAxETlYPyGdv77pdHJ60Lb1KlT8c033wx4/9tvv405c+YEZFEUGOI0aZKJ0ZFDiTfqMUM9iSe6iCKF2OCaI2iD+8sjizExJxnNXXb84d0tI55GWrEnsPPZAOVnlpao/JnEYnykLMtYf1CJMp2nRpse6qcLlPjI936oCutcMxEbWZyRBIPe90G6oqNtKzvawqaiuZsz8qJEtVZo873bWURHRuNp4WiOjgylE9OV36ufNfEAA4We2IMwVWNwOp2E356oHFx9ceV+dFgjq/jSaXNqMfGRWGgDlPlAD58/EwDw7Nf78Pt3NuOP723B79/ZjHs/3A5AGREQKR2V0RId2dbjwIbyVgDA8R7s37LN8cg1x8MtA9tDmLSxp045RFKckQijH3sQQCkW6iSgus2qzUin0GjrcWjzIEsyE3HGzDx8fN2xKM1ORrvViU+31oZ5hdSXiI30ZTa7yaBHjprUFI37D6FR62jj9c1QFliUvx9rGR9Jo5TXVyV33nknfvvb3+Khhx6C2+3Gu+++iyuuuAJ/+tOfcOeddwZjjeQjcaqMsxGGJ+a0/W9zdUS1sYtNtzmCTgMb9To89pPZMOgkfLqtFm+vrxzysT12F1arg8gDNZ9NKMlUTi5+vq0uoM8bCfY1dKGl2wGTQYfp+ZZBH3NsaRbyLfFo63Fg6fbw/Rnsa1BjIzP9O0kqCm07a9rhZCZ/yG2vbsexDy/Hose+Dsswe/JOb3Sk7x1t0TyQPJaiI4PptCzl9ePr5g50uVhEp9DS9iA87Dek02fkYXxWEtqtTrz63cFwL6ef1m7l92ycQRewRIpgOHV6Hi4+ohgA8K+1Ffjn6nL8a20F1qizt4+ZkBnO5fUTLYW2b/c2wuWWMS4rSTuUMxJxaDWUM872qvPZSrNT/H6uJJNBe55NvA4OqTK1my0rxaTNHS5KT8RZs5X5tVuq+POIJL7OZxOief8BKAeiGR05snmc00ajnNeFtrPOOgsffvghvvjiCyQlJeHOO+/Ejh078OGHH+Lkk08OxhrJR4yO9MwF8wph1Ev4aldDRJ2aEhn+4qIzUkwvsOAGNULyng+3D3ki6fsDTbA73ShITcD4LP8KMYe69OixAIDnv9kf8RtWb61Tbw7MLkodMpJBr5Nw/nylq+3fa8tDtrZDiY628X4OIS/JSEJSnB42pxv7G3lBFmobypVu3sqWHpz3zCq8/v3BiDp0QP2JjrZ8P6Ijo3kguehoy+EGd1hTkuJRFB8Hq1vGiuaOcC+HRplOHvYbkV4n4doTSwEAL3yzP6SxeyMR19aR2s3W1+1nTsEfT5+C606cgBsWTcTNp0zC70+bjAfPnYFfqEW4SCAOTkb6vkXERh4/Mdvjz5mpxkduqWwNxpIGtVc97DfBzz2IIGbNMT4ytERs5KGHNmeoc/N4ADCyiH2Dr4U2Mdetri06O0fbnC7Y1T1ylpHXN0NZYFb+PW/s6IbdzUPUNPr41Gd/7LHH4vPPP0d9fT26u7uxcuVKLF68ONBrIz/1RkfyRWA4E7JT8JuF4wEAd32wDe0REt8i1mFOiLyf328WjseCkjR02py44d8btXiZvsR8tuMmZkGSAhvbcsaMPEzMUSIlXlx5IKDPHW7rDipFj/lDxEYKF8wrhCQB3+5tQnlTeG6WB6qjTaeTMFXtauOcttA72KRsclNMBthdbtz+/lbc8O+NEXXTj3pVBWRGm1Jo67A60dYdGa95ntJmI/DaZliSJOG0TOXG4SeN/L1KoSUKbYyOHN6ZM/NQkpGIlm4Hnv9mf7iXo2mPokKbyaDHFceNw42LJ+F3i0pxzQkT8JuF43HRYWMiqhtP/Fm2R3ChTZZlfL1b2b8dP8nzNBKtoy2E3Ud76wJbaNPmtIWwWEjAfrGXPORQrpj9t7+xK2LuzVDfjjbvoyMBID0pDgDQ0h2dM9pEN5vZoEO8n5G1sWx8oglpBj2sbhk/cE4bjUL87RCjZFnu09EWOZuMSHXNCRMwNjMJ9R02PPLprnAvB1aHC3ancvojkma0CXqdhMd+MhvJJgPWHWzBs1/vG/CYFepGbeHEwMe26HUSrl+kdNX9Y+UBtEbpxdpgREfb/JL0YR9XlJ6IY0uVTfDDn+0M+roGE6iONgCYpsZkbq3inLZQK1MLtTefOgm3nTYZep2E9zdW46y/fovaKD1x6AtZlvHX5Xvx2zc24PKX1+Ki577HWX/9Fmf99Vus2tsY7uUBABwuN+rU+SH+REcmxOmRmWwCAFREUVeb1eVGq1M5RMSOtpGdqhbaPm9sh3OQAzFEwSIOaiRHUPx5JDLodbjplEkAgL99tU87+BJu0dTRFi2iITpyR00H6tptSDDqcdjY4fchfWlFkYaukM0bDHRH22xRaKtoZapDCIkkk7GHHNpMT4rTuqa2Mj4yYvjb0SYKbc1d0XnvRouNNPK1cTg6ScIJGcoh6o8a+O+XRh+vC21paWlIT08f8JaRkYGCggIsXLgQL730UjDWSl7ocbggrhEZ2zKyeKMefzp7OgDg9dUHsV7tKgoXcXJLkoDkCP35FaUn4u4fTwMAPP75bizfWY+tVW3YVNGKZTvqsL+xC3qdhKOCNB/h1Gm5mJybgk6bM6JOAfujocOGsqZuSBIwd8zwHW0AcMspk6CTgP9trtFOoIZKp82JWvWG//hM/ze57GgLHzEfoSQjCb9eOB5vXnEEslNM2FvfGTP/tjyx5kAzHvlsF/63uQbLdtbju/1N2FTRik0VrXh5VVm4lwcAqG2zwi0DcXodMpNMfj2X2CRH00DyBoeywY2TJKQaeIhoJIdZkpBu1KPF6cIazkmgENKiI9nRNqIzZuThmAmZsDvduPO/2yLiJj8LbYEXDYW2r3YrsZFHjs/wqhswI9mkddmH4sBcU6cNzV12SBIwPiswhbZJuSmIM+jQbnWiPIqui6LdAa3QNvDnKOI8GR8ZGWRZ9rujLS3aC20ONVUjQu/PRZIfqbOi/9fQCncEXNcQhZLXhbY777wTOp0OZ5xxBu655x7cc889OOOMM6DT6XDNNddg4sSJuOqqq/D8888HY73kIbHBlSQgMY43ozxx1IRMnD+vELIM/OHdLXC4wpcn3GFV57OZDNDpAhu7GEjnzS3AadNz4XTLuPTltTjzqZU466/f4vJX1gEA5o5JhTlIM+Z0OkmbFffSt2Vo6rQF5euE0vqDSjfbpJwUj25uTC+wYMlRyry6O97fCqvDFdT19XVA7WbLSIqDJdH/n/E0tdC2vbo9Im4yjRZut4yD6g0FcZr0sLHp+OMZUwD0dliOBl/uVG4wHTY2HX8+dwb+cuFs3KB2zkbKTZcqbT5bvN+vDUXpyiY5mjra6m29G9xARxLHIoNOwiL1ROmnjI+kEOpidKTHJEnCvWdNQ5xeh693N+CzbeGfF81CW+BFQ3TkV7u8j40UtKJIVWsglzSovfVKN1tBagISAnSfw6jXYWyGch18gPOiQ0KW5T6FtoFjCGYUpAIIbSQpDa2h0wab0w2dBORa4n16jvTE2IiOzGShbUQnpJuRpNeh2ubABsZH0ijjdaFt5cqVuP/++/Haa6/h2muvxbXXXovXXnsN999/P9avX4/nn38ejzzyCJ588slgrJc8pM1n480or/zx9ClIT4rDrroOPLcifJ0cYhOWEqQiVaBIkoQHzpmBWUWpyEiKQ3aKcqJxTHoiJuem4OrjJwT16y+emoPpBWZ0211h/XkFyroypZNyXvHI3WzCjYsnItccj/Lmbjz95d5gLW2A/Y3KJjdQJ0lLs1Ng1Etotzq103IUfDXtVtidbhj1EvL6bJpER+W26nb02ENXwA0nUWj7xRHFuPCwMThrdgF+NCsPgFJoi4QCcLVWaPM9NlIoUjvaounfW506ny3HFNmvjZGk75y2SPg7TKOD1tHGm1EeGZeVjCuPGwcAuPfD7WGfkcpCW+BFekdbu9WhJbocPzHb68/X5rSFoPtIxEaWBig2UijOUA4gHQzT7OvRpr7Dhm67C3qdhDHpAzuk2NEWWcR+IdccjziDbxOIYiU6Mpvx9SOK1+twiroH+bC+NbyLIQoxr39DfvbZZ1i0aNGA95900kn47LPPAACnn3469u+P/pve0Uxs0NjN5p20pDjcrnZyPLlsT9gitdrVjrZInM92qLSkOPz3mqOx/o6TseaPi/Dt70/EiltOwKfXH4cTJnu/UfOGJEm4Ue1qe+W7MjR0RHdX21p1g7tghPlsfSWbDLj7x1MBAH9fsQ976zuCsrZD7asffHi1r+IMOkzKTQHA+MhQOqieJC1KT4Shz1DnwrQE5JhNcLplbKxoDdPqQqeiuRt76juh10lYWNp7krswLRE6Cei2u9AQAV2zVeomtyAQhTbR0RYh3XqeqFc3uJzP5rmF6WYk6CRUWO3Y3jV6Zi5SeHWqB/44o81z15wwAYVpCahus+LJL/eEdS2iGBQN+5BoYY7wQtuqvY1wuWWMy0zCmAzvY+Fmqt1HW0LQfSQ62gI1n01goS209qkF06K0hEELN9PV+d3lzd0xNY89WvkbGwn0FtpauiPz9+BIGuyMjvQG4yNptPK60Jaeno4PP/xwwPs//PBDpKcrN4e7urqQkpLi/+rIZ4xs8d05cwowd0wqbE43vtpVH5Y1iI42M29QjOiESdmYVZQKq8ONZ7/eF+7l+KzH7sI2dXPqTUcbAJwyLRcnTc6GwyXjD+9tDUnXwj61QBOojjYAmJSjRJztrusM2HPS8A409c5n60uSJMwvVl7TRaRpLFuu/q6fV5zWLwo1zqBDnkUpapVHwI2X6rZAdrSJ6Mgo6mizcYPrrUS9DselK9fkn3IgOYVI7z6EB/48lRCnxz3q7OMXvzmA3XWhOTg1GHa0BV6kd7SJ2MiFPsRGAsCMAuWG6sGmbrQF+SZ68AptyrXwwSZGR4bCcLGRAGBJNKJELX6GooBLw6tUo+bFjGdfpCUpvwdbu+1wuaOv8CIO/GXxwJ9HjlfjI6sYH0mjjNeFtjvuuAM333wzfvzjH+P+++/H/fffj7POOgu33HIL7rrrLgDA559/joULFwZ8seS5bjXqi0PIvSdJEg4bmwEgfDf8O6Kooy3c+na1/XP1QXTbh47b+WJ7HS54dlVEdnBsrGiF0y0j1xzv9QWsJEm456xpSDDqseZAM95eXxmkVfbar85oC1RHGwCU5igb5nDeXBptxKndQwttQG/Bd53aaRnLlu1QCm0nDtKFG0knnMVp0gI/NrlCoRYdGRmxmJ6oF9GR3OB65VQ1uoVz2ihUtOhI7kO8ctKUHJw8NQdOt4zb3w/NwanBiGJQKvchASP2dDanO6QzlT0hy3Kf+Wy+pZFYEo3a9VKwiyK9hbbAHuzWrvcicJ8Yi8S877GZQxdMZxSmAghNJCkNr7ejzY9CmzqjzS1H9rzKoYiOtmwe+PNIgl6HxeqsaMZH0mjidaHtiiuuwNdff42kpCS8++67ePfdd5GYmIivv/4al19+OQDg//2//4d///vfAV8sea6T0ZF+mZSrXPDtCtMN/3armNHGF3FPHFeaicK0BFgdbqzY3Tjk4x7+bCfWlrXgrXUVIVydZ0TX0LySNJ/mKhamJeJ3i0oBAA98vCOo8z3cbhkHAjyjDQAmqoW2PexoCxlxmrQkc2AMyPwSpdC24WAL3FF46tBT3XYnvtvfBGCEQlsE3HipUme0FQagoy0/NQGSBFgdbjR2RkckjxYdyRltXjk5wwIdgC2dPaiwRsfPmqJbp5WFNl/d9aOpiDfqsOZAM7ZVt4dlDe3saAu4FJMB4vI+0m4w76rrQG27FfFGHQ4f63l8/aFEV9vmqtYArWygDqsDNW1KDHLAO9rSlUNn5c3dMX3dGym0jrZhDm3OLOCctkgRiOhIo16n3d9qisI5bY3qPiSThTaP/Tg7FQDjI2l08arQ5nA4cNlllyE/Px9vvvkmNmzYgA0bNuDNN9/EUUcdFaw1kg8YHemfiTnKCbnddR1hOU3aGx3JDa4nJEnCKdNyAQBLt9cO+pi99R1ah2Ikxk+sLVPns3kZG9nX5ceMRVF6Alq6Hfh8e12gljZAdVsPrA43jHrJr1NthypVT6bub+yEw+UO2PPS0EQ8TvEgHW1T8sxIMOrRbnVqg+dj0aq9TbA73ShITUDpIDdtxogbL2GOEpJlGdWtgYuOjDPokGeOBwBUtIS/iOiJOp4k9UlmnAGHWZS/x5+xq41CoEu9GZXCfYjXCtMScbiarLE+TB3lWnRkIvchgaLTSdq+LtLiI0U325HjMhBv9P2Q7szC4BdF9qldUFkppoAXgvNT42HQSbA73aht50zTYBOFtvFDREcCwAzxdyoC9+6jTSCiI4G+c9qiq9AmyzIaGB3ptb7xkT8wPpJGCa8KbUajEe+8806w1kIB1MXoSL+Mz0qGTgJaux1o6LCF/OuLjjZGR3pu8dQcAEoE3GBFmk+29BbgtlS2RVRUmsstY0O5cjNlfonvJ0mNeh3OmV0AAHh/Y1VA1jYYscktzkiCQe91Y/SQClITkBinh8Mlcz5CCLjdshaHOHaQQptRr8OsImWDu64sduMjv1Tns500JXvQbtJI6Whr7rLD6lB+t+WlxgfkOQvT1TltEdCt54l69RBRNje4XtPiIzmnjUKgi9GRfpk7Ru0oLw9zoY37kIBKVQuXzRHWySFmkvsaGynMKEgFAKw+0IymzuDsn0Vs5GAHo/xl0Ou0IkIkxIXHMofLjXL12nO4jrZp+WZIkpLo0Bikv1M0MlmWUaV2tBWl+97RBvQW2iLt9+BI2pwu2NX7R1lGXtt4qm985AcNreFdDFGIeH2H9Oyzz8b7778fhKVQIPVucBkd6Yt4ox4l6umqcMxp02a0MTrSY/NL0pGRFIe2HgfWHGge8PGPt/YW2pq67Khui5yTirvrOtBhdSIpTo/Juf7NGzhrjlJo+2ZPY9A2JPsbRGxk4OazAcppX7FxDtd8xNGktt0Km9MNg05C/hCFm/nFSuF33cGB/6ZigSzLWL5TucF0wiCxkQAwRt1Qlntx06W1246z/votXvhmv/+LVFW3Kr+zslJMMBkC89pepMa/iDiYSOaSZTQ41BltvHnvtVOzlELbd22daHEEL1qYIs+XO+tw9wfbsFy9mR4KnUzW8Mvc4lQA4Sm0ud0yC21BMkGNW98apkjQweyu69AOUy2cmOXXc80tTsXYzCQ0d9lxzRsbgpJO0TufLfCFNqA34YEH/oKrorkbTreMBKMeOSlDHx5LiTdinHpPhl1t4dPQaYPN6YZOAnIt/h32S1fntLVEWaFNdLOZDTrEB/Cg8WjwIxEfWc/4SBodvP4NUVpainvvvRfnn38+HnzwQTz55JP93igyaIU2xiv5bKIaYxeOOW1adCQ3uB7T6yQsmqJ0tS3d1j8+8kBjF3bUtEOvk1CUrpxU3FLZGuolDmmdGg00Z0ya3x1i47OSMaPAApdbxsdbagKxvAH2qYW2cQGczyaUqrGtnNMWfGVqZMuY9MQh/97NU+e0hSu+Kth21HSgpk2ZS3LkuIxBHyM62pq67NrN45Gs2NOITRWtePW7gwFba1WrUugrCEBspCBObkdDR1uzwwmXDEgAMo18bfRWSYIJU5LiMTkpHjW2yIoto+BauacJL68qw8o9Q8+wDSSny61137KjzTezi1IhSUBFc0/IkzU67U6I8VQstAWWSK1YVxYZh5esDheufeMHON0yTpiUpR0y9ZXJoMdzF89DUpwe3+9vxp8+2hGglfbaW6/sy4NXaIuMFINY1zsjOgk63fCzyWcWpgLgnLZwEgfy8iwJMPp5ryJNdLRFWXRkvRpfn8U9iNdOYHwkjTJe/5Z88cUXkZqaivXr1+O5557D448/rr098cQTQVgi+ULMRkjkBtdnE9XOot21YSi0saPNJ4unqYW27XX9oiE/2aoUnI4an4Gjx2cCADZH0MW62HDPL/F9PltfZ83OBwC8/0Nw4iP3q9GR44NQaJuYo3a01Yf+391oU6Z2aImbCoOZOyYNkqRE6IQjRjfYRIfH0eMzh5xLkhJv1GJOPO1qE/Pcqlp74AzQie4qtaOtIIBzEUX8SzR0tNWr1zXpRgOMI9yUocH9b24pli2YjKnJgfs7RJGvVH1dFZ0gwdZlc2n/n8kavkmJN2oH/kLd1dbWrdxMjDPo/JrXRQOJ6/y1ZS0REWH/p492YFddBzKTTXj4/FkBec7SnBQ89tPZAICXV5XhrXUVAXleQetoC8IeBGBHW6iIQts4D9JRZhQoHfmRtHcfbcQ+IRB7EG1GW5R2tGWxkcFrjI+k0cbrQtuBAweGfNu/P3ARSeQfsclN5gbXZ5NyIqCjLZ4nZrxx9IRMJMXpUdNm7Rcv8akaG3na9LyIG6rc1uPAit3KEHIR0+evH8/Kh04CNpS3ehV35ylRaPNkc+St3o42FtqCrayp9zTpUCwJvTf7YrGr7Us1NvLEKcPPJREFqfJmz268iNkeLreMmgDF1IrZCIHsaCsSHW0tkX+6sE7twsrhBtdnSQGKHKXoIjo/QlVo61RvRsXpdQGLuR2N5oxJBRCGQhtjI4NmRoEFcXodGjttYZ8B9tm2Wrz2vdJ1/9hPZiErxRSw5z5lWi5+d1IpAOD297bihwD9HbY6XNpcrwk5QSq0qdd74f75xLr9otDmQRflTG3v3hrMJYWc1eGC1eEa+YERoFLdJxQGoNCWlihmtEVXukKjGrueyX2IT/rGR0bCQROiYGK4bIzq5BByv03KVS7g99R1wO0O7YtBu5XRkb6IN+q1Qd6fqfGRFc3d2FzZBp2kdLzNVAd1b65si4gX+UeX7kJLtwPjs5Jw+LjAFNqyzfE4Su3c++/GwHa1ddqcqG1XCgfjM4MQHaneEDzQ2DXkbAerwxURP7toJ6IjSzKG3+T2xkdGRtRRoDR32bWbPydMGr7Q5u2Nl76RQ+UBih8KRnSkKCBWt/bAFeLXOW+JuMM8U1yYV0IUXUTnR1VrjxYtH0ycEx0Yc8cor70/HGwN6ddtZ6EtaOKNeq1osC6Mh5eqW3twy9ubAQC/Pm4cjvNzNttgfndSKU6emgO7y43fvL4e9R3+Hzo60NgFt6wkvmQlB64w2FdJZu/1HvcawSPmfY/1oNA2Nd8MnQTUtdtQ1x45M9b9YXW4cOL/fYXjH/kqKg6Xio62wrShU1A8lZ6kvLY0d0VXUkqtug/JNfG10RcnpJthNugwLtGEFmd0FJiJfOVToa2yshJ/+9vf8Pvf/x433nhjvzeKDN12zmjzV3FGEuL0OnTZXahqDW2sVocWHckXcm+J+MjPttUB6O1mO3xsBjKTTZiYm4w4vQ5tPQ5UNIc3Lm1rVRteV0+T3nf2dL8zz/vS4iM3VgV0o3hA7WbLTI6DJTHwfz8LUhOQFKeHwyVrhaC+dtd1YM69n+OuD7YF/GuPNqKjbbjoSACYX6zc7AvnTaFg+Hp3PdwyMDk3BfkjFK+8ndnRt5M0UKeiq9XoyJHW6o0cczyMegkOl6wV0CNVlU2JmMnn6yKRV9KS4pCZrBSoRUd6MDWrcVAs1PhnbnEqAGBzVeuQB4+CgR1twSUOL4VrTpvLLeP6f29EW48DMwst+H+LJwXl6+h0Eh77ySxMyE5GXbsNf/lij9/PqcVGZidDkoITIV2YlghJUg4WNkdZtF00EdGRnhTaEuMMKFXTNWJlTtuqfY2obrOitt2KC5/7Hjtq2sO9pGH1FtoCER2pFMmbu6Oro61WS9bga6MvEvQ6/HDUNPxn9gSkG3mPmmKb13d1ly1bhkmTJuGZZ57Bo48+iuXLl+Oll17CP/7xD2zcuDEIS4x+u+s68MxX+/DBpuqQfc1ONTqSHW2+M+p1WjTe7hCeNHK43Oi2Kz+/FM5o89oJk7Nh1EvYW9+JfQ2d+Fidz3b6jFwAyqDuyXnKxfrmMEZQuN0ybn9/K9yyEvUoOtAC5dTpuTAZdNjX0IVt1YG7eN+nnkAcF4RuNgCQJAkT1PjI3XUDY67+u7EKPQ4XvtnTGJSvP1q43bJWABppkztPLbRtrWqLmogTT3y5U4lsPWmE2EgAGCOiIz0omlkdrn5Fq8B1tAU+OlKvk7TnOzhIYTuSiI62fJ4kJfKamKm6JwTzT8XvPNExS74Zl5kMc7wBVoc7pDdhWWgLrgVqTPzaMBXa/r5iH9YcaEZSnB5/uXAO4gzBCzhKiTdqEZKB+DssCm2i6BIM8UY98szxAHpnGVNgddmcqGtXupk83U+K0Q+bI2T0g7+W7VCi6/U6CU1ddlz0/PcRXUSsbA5cdKToaIu2GW11dpGswddGXyXpmXRAo4PXV1a33XYbbrrpJmzZsgXx8fF45513UFFRgYULF+KCCy4Ixhqj3tqyZjz06U68vb4yZF+TsS2BMXGYG/7BIrrZABbafGGON+JItWj16qoy/FDeCklS5gUIYqhyOC9o31pXgY0VrUg2GfDHM6YE/PlT4o1YNEXp7nv/h8DFR4qoj/HZgZ/PJkxU4yMHK3Cv2K0U2Kpaexjp4oe6DitsTjcMfQotQxmTnojMZBMcLjlmBpHLsoxVe5W/SyPFRgJKhzMAHPRgRlvFIYW1Q//bFz12l3ayOhCDyPuaWZgKAHhx5YGAPm+g1Vi5waXAuvvuuyFJUr+3yZMnax+3Wq245pprkJGRgeTkZJx33nmoq6sL44p9F8o5beJAAgtt/tHpJMxR4yM3hLCjnIW24BKHl/Y1dIWlY0rcj/jjGVM96ibyl/gagShaiWJdaZDmswljMryby0veEd1sGUmep6Noc9oqW4O1rJCRZRnL1RnRj14wC3PGpKK124GfPf99RM7DdrtlVKqH/YoCEB0pZrRFW6GN0ZFE5CmvC207duzAL3/5SwCAwWBAT08PkpOTce+99+Khhx4K+AJjwZQ8MwBgewC7SkbSbWN0ZCBMyhWFttB1tInZCElxehgCGCU4miyeqhSYXlVjGRcUpyNbPZ0I9B2qHJ6iQUuXHQ99uhMAcP2iUuT0WVsgifjIDzZVB2z+0j5teHXwNrmiwH3oDcHGTpv2M7M73WiKsgv0SCI2uUXpiSP+npEkqU98ZGzMaatps6Kpyw6DTsJ0tfA+HBEdWd1qHTHC69CoyEB0tIlutmSTAeYAH8D43aJSGHQSlu2sx8oI7hTVoiM5o40CaNq0aaipqdHeVq5cqX3shhtuwIcffoj//Oc/+Prrr1FdXY1zzz03jKv1nZh/uicUhTb1d14xC21+E3PaNpS3huxrstAWXGlJcdq/x1DfVO+2O7Xrv5PVvVKwlaiFtuYuu/Z3y1fb1ULbtPyRr9v8UZyuHq5iR1tQeBMbKWiHZKsiY8a6P3bVdaC6zQqTQYdTpuXitcsPx2El6eiwOfHLF1eHLVZ2KI2dNtidbugkINfi/z2L9CTlOr7D5oTdGbpYZH+JjjZGRxLRSLy+i5+UlAS7XbnZkZeXh3379mkfa2yM3Bs04TQ5NwWSpLxIBWIQsCc6tY42Ftr8IW7476oNXaFNm8/GDa7PFk/NgSQB4jr8tBm5/T4+oyAVgHKx7g5QAcobD3+2Cy3dDkzOTcGSo0qC9nWO///s3Xd8VFX6P/DPnZree0gooYReQpcmYAFRERRF7Iquoq667v6W/a7rus1ed13Xgl0WRcGydmkKgkDoLXTSE1Inder9/TFzbxJIIDO5U+7k83698tolmXLiyczcc57zPM+AJESH6lFeZ8aW45WKPOaxcu9ntEknVc8McJ8ZBCj2ce/EYCJtHpyvP5tkdC/fn6r3pn2ugG3fpAiE6M+f+Z0UaUSIXgO7Q0RR9bn/7qQ+blmJ0kZN109Ety4bqXRfkqzECNwwvicA4K//OwCbD3sBuUMuHckebaQgnU6HlJQU+SshwZkRX1tbi2XLluG5557D9OnTkZOTg7feegs///wztmzZ4udRu6+vq9TaMR8G2jIZaOsyqU/bjnxmtAWT0X7q05ZXWgdRBBIjjUiMNPrkOSOMOiREOJ+rK9dDtU1WuU/UINchZm/pmeDqy8tAm1dIvULdCbQNTI1y7adZVH/Qcq0rm21iVjxCDVpEGHV4+7YxmJgVjwaLHUtX7Q2oYGKB63WXGh2qSD/5qBA9NK6lTE2jOuaywW6HyRUUZEYbEZ1Pp98p//KXv6ChoQHjx4+XT3vOnj0bv/nNb/D3v/8dt912G8aPH++1gapZmEEnX0gcLPF+wEYURbnHF0tHds0AKbPmdL3PNh9NrvJYUdxM9FhSVAhGZsTI/750SNtAW7/kCBh0GtQ12+RNcV/ZVVCDFdvyAQB/uXKIV7MWDToNZg9NBaBM+UiHQ5RPIfoio+1ERUObk24/Hj7d5nbnC3hQx0665rFXfOcWuVKpo9xT1QG1+POU1LewM9lsgDOrT9o0Pt97hrSRNKmvc8Pe1GxDbRcbfktB5bQY72S/PjCzH6JD9cgrq8OH2wu88hxdUWezo971GZzKk6SkoCNHjiAtLQ19+vTBokWLkJ/v/HzOzc2F1WrFzJkz5dtmZ2cjMzMTmzdv7vDxzGYzTCZTm69AIB1gOVXVCLPNu7025UBbJw9yUMdGZMRAEIDC6iafHdZkoM37RvupT5uUETbQy4GqM/V2Ba5OdKEXrFQ2Mj0mtNPlBj3VktHG0pHecKLCeeCjd2LnA20heq0csC2t9c17obdIZSOnZ7eUrg8z6PDKDTkI1WtxpLwe2wPoYGNhtfMzXanS9RqNIJePrFJJoK3clcQQptUgghWniOg8Ov0u8dhjj6GhoQHPPfccxo0bJ39vxowZ+PDDD9GrVy8sW7bMawNVO+mC1hfNrJusdthcWToRzGjrkh6xoQjVa2GxOXwWkJFKR7I/W9dIPdlGZcYgNbrthaFeq5FPQ+7xca33dzefhCgCV41Mx9jecV5/vlmuIKMSi/mimiaYbQ7otYIizZA7khodggijDjaHiJOuRa7DIeLHI85AW5LrFG4RM9o8Jv137dXJjdDBadEw6jSobrTi2Gn1bzzsL3ZmtA1J6/xmU6Zr4yX/PBsv0gno7NQoeVOgq+UjpaCy0v3ZJDFhBjwwsx8A4LnvDssHPgKFVDYyWqdFuI4HiEgZ48aNw9tvv41vvvkGr7zyCk6cOIHJkyejrq4OpaWlMBgMiImJaXOf5ORklJaWdviYjz/+OKKjo+WvjIwML/8WnZMUaUSkUQe7Q8TJCu9dz9Y1W+W+U8xo67rIED36u7IRd5yq8clzMtDmfWN6Oa//9xbVotnq3cB3a9I+hLczws4kHerqynuP1IJjkBvXbZ6Sqj0wo807Wg5tulcdJcXVakHNgbaaRotcMvbC7LY9oqND9bh8uPOA7PJf8n0+to5ImaRKrv1jXeUjq+rVEWgrdZWNTDHoFa8sQkTBp9OBNukEe58+fTBs2DAAzjKS//nPf7Bnzx588skn6Nmzp3dGGQQG+bBPm7QhF2nUMdDWRRqN0FLGzkflI+WMNi5wu+Tmib3w4Mz+eHL+sHZ/3tJU2bd92qTnky6kvU3Kpi2ube5yJtLxVllQ3szEEwQBfZPalo88UGJCRb0FYQatnKVXXKPehZa/SZsdPTu5yDXoNPKcFPg4C9Qb9hW5+nx0MqMN6PzGS+v+RNJ9uhpoa8lo816A+4bxPdEnMRyVDRa8vO6o157HEyWuz8VUlmshBc2aNQvXXHMNhg0bhksuuQRfffUVampq8NFHH3n8mEuXLkVtba38VVAQGBmigiAgy/Uefmb/UyVJ73Vx4QZEsjKDIqTykTt9VD7SxECb12XEhSIx0girXcQeH65DpH2IgamRPntOoKVPW1cyxA74MEgoXbtVNlhQF2AHj9ROFEV5Pdkn0b3qKFJ/sBKTetd/Gw6fhkME+idHoEfs2YdRrh/n3E/9cm8JqgOkRKbUfiLLzfk6F6lPm1oy2spc5euTubdKRJ3g1k4po/eekwNtPshoK2x18p1z1nVyn7YyHwXamlw92pjR1iUhei1+PbMf+iW3v5iUmirvKfLdArfRYsOx084NriFebuQtSY4KgSAAFpujyzXtj7heA0peaHekv9ynzfnfS8pmm5gVL2dhsUebZxwOEaeqXP0ROlk6EmjJJPRV+Spvqag3o9TUDEFwr3ySHGg7R9DM7hDlEiuZ8WFyRkdXA22FrXq0eYteq8H/zR4IAHhr40nkB9BJbrk/GwNt5EUxMTHo378/jh49ipSUFFgsFtTU1LS5TVlZGVJSUtp/AABGoxFRUVFtvgJFP1eg7Ui5965nC9ifTXEjM109Un0UaKuRAm1eLs/XnQmCgDGuPm2+Kh/pcIg45Do0OtgHWWGtSRltJ7oSaPNhRltkiB7xrkAAs9qU5Qxe2iAI7n9OpEZLGW3qXf+tlctGJrf78+E9ojE4LQoWmwOf7Cj05dDaVVTThI1HnT3SLx+WptjjxrlKRwZKMPF8Ss0tGW1EROfjVqCtf//+iIuLO+eXO/785z9DEIQ2X9nZ2fLPm5ubsWTJEsTHxyMiIgLz589HWVmZW88RKKSLwuOn671eIqLAtcmXwUWuIqQ+bUfKvN9AHoB8co4Zbd41rEcMAGB/US3sDt/0nDpYYoJDdAYskqK802vpTAadBomu8nUlXcwAazkJ6/1Fbn/5defcFNiQ5wy0Te2fiHTXCcBiFS+0/KmsrhnNVge0GsGtUoSJrkDb6Tqzt4bmE1J/tt4J4W5lfctBs3NsuhTXNMFqF2HQapAaHSp/DudXda3cZpEXyra0Z3p2Eib1TYDF7sAT3xz06nO5o1gOtBn8PBIKZvX19Th27BhSU1ORk5MDvV6PNWvWyD/Py8tDfn4+JkyY4MdReq6vDzLapE1pBtqUM8oVaNtTWAurD/pFs3Skb0h92rb7KNB2qqoRjRY7jDpNp/vzKqWXq0fbSQ97tFlsDvl9y1dlLzMVqkhAbUmHMVKjQhCid68UeIocaFPnOsTuELHB1W98+hllIyWCIOD6cZkAnOUj/d0X++PthRBFYEKfeEX7rsqlIxvUkTEqlY5M5oE/IuoEt1JmHnvsMURHK5uFMXjwYPzwww8tA9K1DOnBBx/El19+iZUrVyI6Ohr33nsv5s2bh02bNik6Bl9IijQiLtyAqgYL8krrMDwjxmvPJV3AZLSTjk7u65/i44y2ZimjjR/k3pSVGI5QvRYNFjtOVNSjb5L3y6hI5eqGuFGuTglpMaEorzOjqKYJQ3t4/txy2RYfnCaVMhEPl9Wh3myT69lP6Z+IeldDYma0eUYqG5kRGwq9GyVAgyXQts+VxTrYzazSnq6NqfyqRoii2G7GuLQh0yMuFFqNoEhGm90hotRVJsebpSMB5wL/j3MG4tIXfsJXe0tRUW+W+8z5U7GrRxtLR5KSHn74YVx++eXo2bMniouL8eijj0Kr1WLhwoWIjo7G7bffjoceeghxcXGIiorCfffdhwkTJmD8+PH+HrpHfBFok0vnKrgh1931SQhHdKgetU1WHCwxyQfFvMHhEFk60kdGuzLack9Vw+EQodF4twqN1J8tOyXSq+Xf2yMF9qobrahttLqdLXnsdD0sdgciQ3ReP3Ak6RUfjp35NcxoU1ilqyeXtKZwh9yjzaTO9d/O/GrUNFoRHarHqMyYDm935Yh0/OPLgzhe0YDNxysxMSvBd4NsxeEQsTLXWf762jHK9puNC3e+B1SrrHQkM9qIqDPcCrRdd911SEpq//SFxwPQ6dotwVJbW4tly5Zh+fLlmD59OgDgrbfewsCBA7FlyxbVLXIFQcCg1ChsPFqBgyUmLwfanBcfGXG+uRANdlJG24mKBphtdhh17p2+cpe0wI1k6Uiv0mk1GJwWhe2nqrGnsNYngba9rg1+Xwfa0mNCsaugpkuBKYvNIZe99EVvB6l05MnKRvx4+DRsDhE948PQMz4cNa6L8op6C5qtdrdPRHZ3J12le3q6eaI5KdK5wC1XeaBNyswc4mbAOD0mFBoBaLLacbrO3G5WqrQh09MVYFOiR1uZqRl2hwidRpDnwJuyU6LQPzkCh8vqkXuqGpcM7rhMnq/IpSN5AIUUVFhYiIULF6KyshKJiYmYNGkStmzZgsTERADA888/D41Gg/nz58NsNuOSSy7Bv//9bz+P2nP9XNc5xysaYHeI0HphY196r2NVDeVoNAJGZsZgfd5p7DhV7dVAW73FBqnIAwNt3jUoNQphBi1MzTYcKa/HgBTvXlv7sirFmcKNOiRFGlFeZ8bJygYMD4tx6/5y2cjUKJ+1xZAOSnWlrxydrbLBuYaI9+AQl9yjrVadJeylspFT+ieeM9gdYdThypHpWP5LPpb/ku+3QNuW45UorG5CZIgOlw5Rdi0QGyZltKkj0CZltKXwwB8RdUKnjzN566LmyJEjSEtLQ58+fbBo0SLk5+cDAHJzc2G1WjFz5kz5ttnZ2cjMzMTmzZs7fDyz2QyTydTmK1BIm9Pe7tMml45kRpsikqOMiArRwe4Qcfy09y+2TSwd6TNSdpevGpFLmTTubvB3Vaq8MPE80Ha0vB5Wu4ioEJ1X+0RJUqJCEGl0vu7e/vkkAGfZSMC5+RNmcAbX1LrY8icp0NY7wb1AW9BktBV7FvA26DRyRllHfdqk3ndSEFPaqCmuafa45JcUIE+JDvHKxnh7clwlraRMUn9j6UjyhhUrVqC4uBhmsxmFhYVYsWIFsrKy5J+HhITg5ZdfRlVVFRoaGrBq1apz9mcLdOmxoTDqNLDYHHL1C6XJGW0MtClqlNynrcarz1Pb6HyvNeg0PMTkZTqtBiNdWS2+6NN20IdVKdrTy3XNedKDwJUvK2pIpHKXzGhTVoUroy0u3P3rudRo5zV4aW2z30sqeqKlP1vieW97/Vhn+chv9zurS/jDh9ud2WxXDE9T/PNAmn/1ZLQ5q+mwdCQRdUanA23e+DAbN24c3n77bXzzzTd45ZVXcOLECUyePBl1dXUoLS2FwWBATExMm/skJyejtLS0w8d8/PHHER0dLX9lZCib5twV0sXhQW8H2niaVFGCIMj9og77oHwkS0f6zjBXoE3KNPOmZqsdR1zlmvxROhJwbvZ7SlrkZvvoNKkgCOjnymrbesK5ATGlX6L8s5bfSZ3lQ/xJ6pHhbmmvJFegTc0ZbaZmq7xpMtiDDRvpv1lHGy/5Z/QnSowwwqjTwO4QPf5blZ7LFwFuyeiezk1dX2z+dUZxM0tHEnWVViOgT6Lzc/WIF8pH2uwOuZ+kkr1cCHJAZmeBdw8/sD+bb+X4sE+bdB3vj4w2AOjlek844UGfttYZbb6SGecMDDKjTVlS6cj4CPcDbVLpyEaLXd4vUYuimiYcKq2DIABT+5+/QtiQ9GgMz4iB1S5i5fZCH4ywrdpGK77e59xzXTBa+f1UKdAm/T0EMlEUWzLaWDqSiDqh04E2h8OheNnIWbNm4ZprrsGwYcNwySWX4KuvvkJNTQ0++ugjjx9z6dKlqK2tlb8KCgoUHHHXSBe2B0vq4HB45xRObZNVvvDwVQ3z7kDu01bqg0Bbk5TRxtKR3jY0PQYAsL+4FjYvN5g/VFoHu0NEfLhBzjDzFTko1YWMNvkkrA8XuVKAGwD0WgETsuLlf0u/UxEDbW6TAje93Cwd2TqjTY0nSYGWzZr0mFDEhLm/yJd7rnWw8SKXjnRtKGk0gnzoxdPykd/udy50R7oyGnxhTC/n5t++olo0W+0+e9721NnsqHO9P6cx0EbUJf282KetpLYZNocIg06DZB+Uue1OpJ6iBVVNcp9ab2B/Nt8a4+rTtuV4Fcw2733WVjdY5AoQ2V4uUdkROaPNzUCbKIr+yWhzXceVmJr9fh0UTKTSkQnh7peODDVo5femMpO6Kpqsc2WzjcqM7XQ23yJXVtt/t+Z7be+wI5/vKYbF5kB2SqR8MFlJaspoq7c70OhahyQZuT9HROfn20645xETE4P+/fvj6NGjSElJgcViQU1NTZvblJWVnbNsi9FoRFRUVJuvQJGVGAGDVoN6sw2F1d7ZHJay2eLDDQjnB4FiBvgwo63OFSiNZEab1/VJCEdMmB7NVgce+HAXLDbvBdukspGD06N91l9Akhbj3PDqSvaXPwJtfV0bggAwumdcm/e0dAV+p+5IFEW5bE8vD0tHNlntXt3o8ya5fGu6Z3/H8gnndoJmoijKJ59bZwv27EKgrarBgnV5zsX5vFHpbt/fUxlxoUiMNMJqF7G7oMZnz9seqT9blE6DCC/3SCUKdn29GGiTDhpkxIZC46Myt91FXLhB/gw+4sW1iJTRFsNAm0/k9HRuupeamvH4V4e89jzSNXxmXJjf1pe946XSke5dCxXXNqO2yQq9VpD7TPpCXLgBEUYdRBEorGb5SKV0JaMNaN0OQV2BtvV5UtnIzicuzBmeikijDvlVjfjpaIW3htauj7Y5kxWuGZ3hlX2L1j3aAv3wZmmrdUi4lusQIjq/gAq01dfX49ixY0hNTUVOTg70ej3WrFkj/zwvLw/5+fmYMGGCH0fpOb1Wg/4pzgXugRLvlKqTLgR7sGykolpKRyq/MXEmOaMthIFSb9NoBDx+1VDotQL+t6cEd763HU0W75xalDb4h3q4wd8VUvZXeZ3Zo2CiKIryIt2XJWdaZ7RN6d+2nn1aNEtHesLUZEOz1fk34G5mZZhBhwhXsFOtfdr2uzLapOwAd52rdGRlgwUNFjsEAejRqkdqVzLa/renGFa7iCHpUW1eD94mCIJ80n67n/u0FZulspHsz0bUVS2BNuWDNXJ/NjezpalzfHHoj6UjfSvMoMOz1wwHALz980l8s6/j9hhdccAPh+XO5GmPNqkSQd+kSBh0vtu6EgRBrmLAPm3KkfqNxUe4n9EGOPsVA0BpF6q0+MOuAuc+QOvqLOcTZtDh6tE9AADPfX/YZwGpA8Um7C2qhV4rYO6INK88h5TRZrY50BTgGaNlrrKRySwbSUSd5NdA28MPP4wNGzbg5MmT+Pnnn3HVVVdBq9Vi4cKFiI6Oxu23346HHnoI69atQ25uLm699VZMmDAB48eP9+ewu2RgivMC90CJdxZJBVXOi44Mlo1U1ABXmY38qkY0eDGTw+EQUW9x9WjjItcnZg1Nxes3jUaIXoP1eadx05u/wNRsVfx59hW7Mmk83ODvivhwAww6DUTRs1IbZSYzqhut0Gpa+qb5QuvAwtQzA20K9J3rjqSSLRFGnUeNrRNV3qdtf3FXM9o6DppJGzEpUSFt/ttK9ynwIND2yY4iAMC8kT3cvm9X+bJ3zLkUu06SsmwkUde1Lh2p9KbdqSrnBnomD/t5hS8O/THQ5nsXZifhril9AAC/+3i3R9cK5+Pv/mxAy0GlmkYratwoF+eP/mySXgnOMbubhUcdq2xwZbR1snzimdSY0Xa6zoyKejMEwf3SrXdPy0KYQYvdBTX4aq93AvFnWpnrzGabOTDZ44Do+YQZtHLgvKohsMtHShlt7BNNRJ3l10BbYWEhFi5ciAEDBmDBggWIj4/Hli1bkJjo3FB9/vnnMWfOHMyfPx9TpkxBSkoKVq1a5c8hd5lUW1y6aFRagSujLYOLXEXFhRuQHOW80JAye7yhzmyDtO8RyYw2n5k2IAnv3T4OkSE6bDtZjete3SKfuFOC2WaX+/sNSfd9oE0QBKRFe15qUcrAzUoM9yg446nkKCNumtATC8dmYGBq24VJS6BNXSca/U1azMSGe7ZYaN2nTW2aLHa5XJqnAW9po6iqwYK6MwLy+R1sMnt6IvrY6XrsLqiBViPgCi+dKD2X0T2dGW25p6p93huitZJmBtqIlNIzPhxajYAGi13xjUopQMBAm3f0dx108kVGGw/7+dbDlwzAyMwYmJptuO+/O2FVuG+0HKzyYY+zM4UZdPJa+oQbfdqkNYg/xi6VC++oLy+5x+EQUe1ahyR4GMBJjnKuZ9XUo+1QqfP11ys+HGEG9/Z3kiJDcNeULADAU98e8mqbC8C5Z7F6p/OQ34IxGV57HkEQEOcqH1ndoPwBZyVJJeyTuQ4hok7ya6BtxYoVKC4uhtlsRmFhIVasWIGsrCz55yEhIXj55ZdRVVWFhoYGrFq16pz92dRAOknmrWCNtMjNiOUiV2nSxqxUAtAbpLKRIXoNjOxF41NjesVhxZ3jkRBhwIESE+5+P1exxz5SVg+rXUR0qB49/JRtKgemPCi1cdCVgevrk7CCIOAvVw7B4/OGnVUfPt31+xTVNAV8bfdAIp0kjfOgCTmg7oy2g6UmOETn4j4pyr2ymZLIEL1c7uTMwJn079b92QAg0/Xv/MpGt/5WV7uy2ab2T/R4Q6IrBqVFIVSvhanZhqOnvV82uSMsHUmkHINOg16u9ySl+7RJ74EMtHlHf1cmhHRwyxuY0eYfeq0GL103ElEhOuwqqMEz3+Yp9tgWmwPHXJ/hZx5a87VerrKy7hw88mfZS+m9sr2+vOQ+U7MVNtfBrbhulNHW0n7Bs9ffHZN7IzHSiFOVjVj+yyklh3aWz3YVo6bRiuQoI6b0Szz/HbpA+huociPD1R+k0pEpLB1JRJ0UUD3augNpo7qopgm1jcqf3iisdpWOjGPpSKUNdmUi7fNSNiIAuWShvxpVd3eD06Lx4V0ToNMI2HayWrGG83vl/mzRXmko3Bmp0Z6XWgyEkjNnSo42QhCctd0rA7zkRCCp6mLJliQVZ7RJ/dk8LRspkTaRD52x2Zlf2X5/IungS53ZJm9ino/DIconSueNSu/SeD2l12owIiMGALDNj+Uj5dKR/FwkUkTfVuUjlSKKYqv3QAbavEEq+1leZ5azQpRWw0Cb32TEheGpq5392l798TjW5ZUr8rhHyutgtYuICtHJh9T8pberT1tnM9pqm6xyWwx/BNqk67ncU9Veq0bUnVTUO9+3okJ0HvfbS3GtZ0tVFWhzHVhN8exvONyow4Mz+wMAXlp71CstLgBnUP6lNUcAALdd0BtajXf3LORAW0NgrylLmdFGRG5ioM3HokP18kXuwVJlL9hEUWwJtDGjTXFDXCUrvJvR5urPxrKRfpOVGIFpA5IAAKtcG91dJf3NDO7iBn9XpMd4XjryYAAG2ow6LRJdWT4sH9l5VXJGm2eBNjWXjtxfpEyfxCmufoFvbTrRJkNNOvF85iZzqEErByjb6+3Wnl9OVKGopgmRITrMHJjcpfF2xZhervKRJ6v9NoYS9mgjUpQUaDuiYKCtptGKOlcPY5av947IkJY1pLfKR5oYaPOrS4ek4OYJPQEAT32Tp0jFhtZVKfx12E8iBa5OdrIU4yHX+iM9JhTRYb7/m8zpGYsh6VGoa7bhutc2I/eUf3vWql2lqy1DV6o0qDujzfN19ILRPZCVGI6qBgv+s/6YUkNrY2VuAQqrm5AYacRNE3p55Tlai5UDbYFdOrLMdW3DjDYi6iwG2vzAW33aKuotaLLaIQhAaoxnZbGoY0N7ODdnj5TXo9lq98pzSD1/2BvBv6QMks92FinSm0jKguzqBn9XSKUj3V2YNFps8slTf5wmPZf02I77tG06WoFX1h9jWckzVNZ3NaPN+dlSXqeeBa5kX7Er4N3FPh+3TOyFMIMW+4tNWJ93Wv6+XDoyLvys+7jbp23VjkIAwJxhqT7ti3imnF5xAIBtftxcKpGbkLN0JJES+iU5y1cdUzDQJh0iSI4y+vU9K9gNcJWP9FagjaUj/e/Bi/rDoNPgYIkJ+4q6vlcQCP3ZJL0TnNdCJzuZ0SaXjfTT2A06DT64YzxG94yFqdmGG97Yip+OnD7/HaldUgWS+AjPr+ekHm21TVY0WbyzH6Ok1qVbs7tQulWn1eD3swYCAJZtPIESD1pBnEuz1Y5/rjkKAFgyLQuhBu9/jse5gufeytBWSqlUOpIH/oiokxho8wPpNMsBhfu0FVQ7F7kpUSHs7+UFKVEhiA83wO4QvdYfwdQsZbTxg9yfpmcnITJEh+LaZmw5Udmlx7LaHfJJtqHp/gu0pcZ0HJQ6l7zSOoiuvlZSNlOgSJP7tLUN+jgcIn69Yhee/OYQ9hR6LwNVjaTyHN0to81ic+BwqXOhO6SLr8O4cANuGO88cf7S2iMQRRENZhsqXCd1M9spmyYF2jqT0dZksePrfaUAgKtG9ujSWLtqVGYMNAJQUNXkl8bzDTY7am3OjRRmtBEpoyWjTblrWSmjl/3ZvKtfsnPuDpd5p2+mHGjzQ/YQOcWEGXDpYGdP+hXb8rv8eAf92OPsTL0SpIy2zh06koOEfhx7dKge794+FpP7JaDJasftb2/HN65rNHKPlNEW72GfaMBZ9SfMFQQq9cN1qbuOltcrVrp15sAkjO0VB7PNgee+O6zQCJ3+uzUfpaZmpEaH4LqxmYo+dkdiVdCjTRRFlLF0JBG5iYE2P5AuFg8qHWhzLXJZNtI7BEGQ+7Tt9VL5SKlkCzPa/CtEr8WcYakAgNU7ulY+8mh5PSw2ByKNOr9uQEmlI4vcDLS1lJzxbwP19qR3EDw8WGqSgx7lKgsIeVtlV0tHRqgz0HakvA4WuwNRITr0iO16j5I7JveGQafBzvwabD5WKWeqxYTp280EkIJvBZ0ItH13oBT1Zhsy4kIxumdsl8faFZEhegxw9ZTY7ofykVJ/tkitBpE8QESkiD6Jzs3u6karvPHZVQVyoO3sjF5SzoBk57VYHjPagtp1YzIAAJ/vKu5S1o4oigHVZ1nK+K9tsnYqi8XfGW2SMIMOb9w8GrOGpMBid+CeD3Lx7X4G29wl9WjrSkabIAhIkctHBn7rgEOuVjHZCpRuFQQBS2dnAwA+3lGI/251LxD/3f5SLH53+1ltUJosdry8zlmO8t7pfX2WlS6tRQM5o63aZofFVR0nycDWLkTUOQy0+YEUaDtSVg+r3aHY40r92XrE+bfRcTCT+rTtL/ZSoM1VOjKSPdr8Tsok+XpfaZdKhe5t1Z9N4+WmwueS6moeXddsk0uUdkYgnYQ9U1p0+33nNh2tkP9/dQCfkvOHqi6WbUmKcgbaqhotXfr8eva7PDz04S7YFPwMPJf9rvJLg9OiFelRkhQZIm+E/WvdUeRXOcsg9ewgmO5ORtsqV3D/qpE9/PqeIZGCfdv9UD6SZSOJlBdmaDlwsCO/RpHHzK9kRpsv9E9uKR2pdGlsh0Nkj7YAMb5PPDLjwlBntuGrvSUeP05JbTNqm6zQaQQ5G9KfQg1apLhK/504T582i82BI67MzUBYgxh1Wvxz4UhcndMDDhH446f7UO/q3USdU+mqqhHfhR5tQEuftlIV9GmT+7OlKHNgdWRmLG6e0BOiCCxdtRfPfdf5Xo4vrjmC7w+UYd4rP+O9zSfl+727+SQq6s3IiAvFNTkZioyzM+LkHm2Bu1aXstni9FoYNdw6J6LO4buFH/SIDUWkUQeLvaVmsxKY0eZ9UskxJWrmt6eOpSMDxuiesegRG4p6sw3fHyjz+HH2uwJt/uzPBgDhRp28ceJOn7ZAOU3anrQOMtp+OtISaKthoK2NKjmjzbNFblyYAVqNAFFs6ffmrnqzDf9cexSrdhZhd2GNR4/hLulwxJB05f6O75qaBZ1GwM/HKrF6pzM4lhnffjZHZwNthdWNcv+PeSPTFRtrV4zu5Qy05Z7yfUZbkdn5N8aykUTKkkrT/WeDMr1MT0mHDdopnUvK6ZsUAY0A1DRaFc8sr7fYILUlZqDNvzQaAde6DvN8uK3A48eRSi/2TYoImLYSvTrZp+1wmbMSQaRClQiUoNNq8PerhqBXfBhO15nxz7VH/D0kVZHWDQldyGgDWvq0udt33B9aKsMot/748xWDcf+MfgCAl9YexcMr98BiO/fBxWarHYdcrU8sNgce+Ww/lizfgZLaJvxngzOb7f7p/WDQ+W57OC4s8ANtpa5AW4qBn4lE1HkMtPmBRiPIH7Z7FewfJPVoy+BpUq+RgiV5pXXnvaDxREvpSGa0+ZtGI2DuCOdGt7SJ7gkpo21oD/8G2oCOA1MdcThEHAqgkjNnaq9HW7PVjm0nWzJvqhs7n70X7ERRbGlE7mHpSI1GkBfInm7ytS6bvFOhbIrz2efabOpqf7bW0mNCMX+UM/P12/3OYPz5MtqKa5rO+dnxyvpjcIjABX3j5V4m/ja6VxwAYH+xCQ0+Pr0tZbSl8fAJkaLunNIHBp0GuaeqsflY13rRAs4+jgDXIN4Woteil+tAh9LlI2td10tGncZnpcOoY/NH9YBGALaerPL4YO4e1xokkK7he3eyT9vrPx0HAIzrHa9IJQKlGHVa/OnyQQCANzeewHEFD00HOynQ1pUebUBLRps/ege7QxTFlow2BV+DgiDgoYv644l5Q6HVCPhkRyFuf2fbOSvW7C82we4QkRBhwB8vGwi9VsBXe0tx4TPrUd1oRZ+EcFzl4wN+Uo+2QK4+U2phfzYich8DbX4yro9z4+qTHYWKPaa8yA2QU1/BKCMuFJEhzmxEJZvIS6TSkcxoCwxXjXJecG44fFru+eUOu6OlN8JgP2e0Aa1LLXZuYVJQ3YgGix0GnQZ9AmTTvzWpR1tFvVku77njVDWarS2BDGa0tWi02OUgj6c92gAgMdK5QC6v82yBu79VbwBfBNrsDlE+1T1Y4czMu6dloXV1x8wOsjkSI40I0WvgEDsOdJfWNmPlduc1wf3T+yk6zq5IjwlFWnQI7A4RuwtqfPrcLaUj+ZlIpKSkqJbyty91MSvDbLOj2NUrhxlt3tdSPlLZDX72ZwssKdEhuHBAEgDgIw+z2n487MyQH9c7TrFxdZUUKD5XRtu+olp8tqsYAPDAzMC5HpJMz07GhQMSYbWL+Ov/Dvh7OKpRIZeO7FpGW4qrHUKgZ7SdrjejssECjdDyvq2k68Zm4o2bRyPMoMVPRypwzwc7OrztHlcFkeE9YnDH5D5Y+auJ6BEbKq+Zfz2zH3Ra324Nyz3aGq1wOJQthawUqXRkCtchROQGBtr8ZOHYTGg1ArYcr2pzut9Tdocob97xNKn3CIIgZ7Xt90L5SFOTM1uAPdoCQ1ZiBIb3iIbdIeKL3cVu3//Y6Xo0Wx0IN2gDIlDlbkab9N7UPznC5xffnRETpkeo69S1VKd/o6s/m84V/ahuYEabRCrNYdRpEGbw/LR6UqQzYOtpRtv+4pb3zh353i9HeKKiAU1WO0L1WvROULZHSa+EcFwxPE3+d0cZbYIgnLd85H82HIPF7sDY3nEY1yde0XF2VY4rq23bSd+WjyxqlkpHskcbkdJ+NTULeq1zLdI6E9xdRdVNEEUgzKD1OFuaOq+/q9fW4VJlD/yxP1vgkcpHfrKj0O2+uFUNFrk89zRXwC4Q9JIz2joOtD3x9SEAwJUj0hStRKCkR+YMgl4rYF3eaaw95HmLge5EqdKRqVHq6NEmlY3slRCO0C6su87lwgFJ+O/i8QCcrRM6KsMoHZQb1iMGADAiIwZf3j8ZN4zPxE0TemLOsLR27+dNMWHOzxq7Q5TbpwSaUotzXCwdSUTuCLyd024iLSYUlw5x9kd4a9OJLj9eSW0TbA4Req0g160m75BKAO4rVq7sp0TOaOMiN2BIZRQ8KR8p9TQanBYNjcb/ZU/kQFtt5wJtB1wLhEBoQt4eQRCQFiNl6Tl/JynQNrlfAgCgihltstZlI7tShifR1cTc00DbvlaBtpLaZpR08u/RU1J/tkFpUdB64XW45MK+AABBAHondhxQlwJtp9oJtJXXNeO/W/MBAL+eEXint0f3dPZp237K8814T8ilI3mSlEhxaTGhuDrHldW2xvOsNuk9LTMuLKBKvAWr/inOzAjFS0cy0BZwLsxOQmKkERX1Fqw5WO7WfX86chqiCGSnRCIlOnD2BqSMthMVDe32h/zx8GlsPFoBg1aDhy8e4OvhdVqfxAjcNqk3AOAvXxyA2Wb384gCm8XmkN9julo6Uvp7DvSMNm+UjWzP8IwY+QDG1hPtl4Le42pXMzyjJXAdHarH3+YOxV+uHOKV9dH5GHVaRBidh8sDdb0uZbSxdCQRuYOBNj+67YJeAIBPdxWj0oOydK1JZSPTY0L98kHZnUilx/YVKR9ok07zsHRk4Lh8eBp0GgF7CmtxtNy9Mj0b8pwlWy7om+CNobntzKDU+Ujl9gKpt8OZWvq0NaGm0SL3xJNO5rF0ZIsqV8mWuC6eJG0pHen+55bZZscR1+agdKJ1l5fLR0rv1UqXjZT0S47Ev64fiWeuHi5n+7Wnb5Jzc/S1H4+ddQr39R+Pw2xzYFRmDCZmBVY2GwCM7hWLpEij3BfDV+TSkfxMJPKKe6ZlQasR8NORCuz0MMO4oFWgjbxvgKsE2ZGyOkXLbTHQFnj0Wg2uznH2gv1wW75b9113yBmYuzA7cLLZgJbysnXNtrP6KDscopzNdsP4ngFfpee+6f2QGGnEycpGvLnxpL+HE9CkPlxajdDl9xgp0FbZYD5n32N/k/qc++LA6nhXJYwtx88+EFfbZMVxV6lWKaMtUMSGO/8WOsrE87dSqXQkM9qIyA0MtPnRqMxYDOsRDYvNIZ9k91RhtXORG+gXpMFAKmFxoMTZVFZJUkZbdChLRwaK+AgjpvZPBAB8tqvzWW1WuwObXNlV0wYkemVs7mopHdm5E4C+OonXFemtfqefj1VCFIF+SREY4DrxfeYivjuTSrbEdfEkaVKU5xltR8rqYXOIiA7V45LBzqxub5ePlEpVDvFin8Q5w9Iw37UZ1pHbJvVCz/gwFFQ1YdEbW+S+j5X1Zry/xXkNcP+MfgGZETIoNQq//GEGnrp6uM+es8FuR43rhDhLRxJ5R0ZcmJy5/8+1Rz16jFOVzjUI+7P5Rq+EcOi1AhosdhR18uBUZzDQFpgWjHZmnW44fLrTB+XsDhE/HnGtQfoHxhpEEqLXyj2jzzzA+NnuIhwoMSHSqMO90/v6Y3huiTDqsHRWNgDgn2uPoNwU2BlW/iRd88aGGbpc5SUuzACDVgNR9LxftC9IpSOzU5Tvz3amlkDb2Rlte13ZbBlxoV3q0e0N0pq0OkADbWUWZrQRkfsYaPMjQRBwqyur7b0tp9yuvd5aQbXzwrtHLBe53tY7PhzhBi2arQ4cP61cI3JRFOX+CMxoCyxzhqcCANYe6nzZltxT1agz2xAfbsDQAOkvIGWjlNY2n/cUdEltk7yBMzAlcANtrfvOSWUjJ/VLQKxrIVHTaGm3NE13VNWqdGRXSKUjPVncts4uG5XpLEe404sZbaIotjxnun//jpMiQ/DBHeOQFh2CY6cbcOOyrahttOKNjSfQZLVjWI9oOagfaARB8HkAUMpmi9BqEKXzTm8LInKWv9UIzmscT6o15DOjzaf0Wg2yEl192hQsH1nTxPL1gah3QjjG9Y6DQwQ+yS3s1H32FNagqsGCyBAdRrlKPweSPq6/31ve2oq//u8Aimua0Gy145lvDwMAfjUtK+ACAh2ZOyIdw3pEo9Fix5d7S/w9nIClVH82ANBoBCRHO9cigdqnzWyz45hrn8gXB1bH9nb2Uj5UWndWdpjUq3F4gGWzAUBcWOBmtDlEUQ60pRh5CJ6IOo+BNj+bPTQVCRFGlJnM+HpfqcePU1glZbSFKjU06oBGI2CQqwTZXgXLRzZY7JBiH5EMtAWUKf0SIQjOzJjOBhfWu8pGTumfGBD92QAgOSoEGgGw2B2oaDh3NtK/1x0D4Lxwjw4L3L/H1n3nNrpO707qm4BY15itdhENFvZNAFoWMV3dvJAz2jwoeSxnl6VHY2RmDADn+6i3Sr8UVjfB1GyDXiugX5L3T5SeT4/YMLx/xzgkRBhxsMSEm978Be/+fBKAswRRIGaz+UuJK8M7ladIibyqd0I4rhjuLLf8z7Xu92rLd2W0ZcZ33KOSlNXfVT7ycJlyB/6Y0Ra4rnFlta3aWdSpw2PSGmRyvwTotYG33fPQxf0xMDUKjRY7lm08gSlPrcO1r21BUU0TUqJCcNsFvf09xE7TaARcNtR5IPPHw6f9PJrAJR/2UyDQBgCpUc71X6D2aTta3lLBwxdl1xMijK36tLUtH7m7oAZAYAbapIOxgdijrdJqg110bpgn6vm5SESdF3hXXt2MUafFDeMzAQBvbTrh8eMUuEpHMqPNNwa7SpDtKzIp9ph1rk1FvVZAiJ4vzUASH2GUs9J+PFzRqfusz3NmvwVK2UjAeQo6OcrVQPoc5SOLapqwwtUL4sGZ/X0yNk9JpSN35dcgv6oROo2AcX3iEarXwqBzvo4CtRyFr1UqFGhLjHD+DZWbzG5nC+4vbslo650QjpgwPcw2h1ymVGnS8w1IiZT/HvytT2IE3r9jLGLC9NhdWIsGix0DU6Mwc2Bg9VHxtyKz8++VZSOJvO/e6X0hCMC3+8vkPpqdUdVgwfEKZ7CnTwIDbb4ibagqmdHGQFvgmjUkBWEGLU5UNGBHJ6oArHcFfKb1D8zrilGZsfjq/kl457axmNAnHjaHKAcDHryoH0IN6spin+KqRrDleBXMNh7ua49UOjK+i+XrJcmu4FVZgJbrbF020leH6DoqH7nHVTpyeEaMT8bhjrgw5zV+IK7Vpf5siQYddAFyaJqI1CEwdp26uUXjesKg1WBnfg12uS4y3VVQ5SzxlhHLjDZfkPq07StWLqPN1GQD4CwbyayGwCP1OJACaOdSWtuMQ6V1EARgcr/ACbQBLeUjz9Xn4V9rj8BqFzExKx4TsuJ9NTSPSIG2OrPz9TMyMwYRRh0EQZCz2mrYpw2AchltiZHORbLZ5pD/u3eG3SHKC8/BaVEQBAEjXYu+nV7q0yYdhvBmfzZPZKdE4d3bxiLCVYrk1zP68n3/DFLpyDRmeBN5Xd+kSFwyyNk385UNxzp9v893FcFqFzEkPYp9on1IymjLK1Uu0GZioC1ghRt1uHSI8/X5yY5zl4+srDdjj6tU3NQAOux3JkEQMLV/Iv5753h8tuQCzB2RhuvGZGD+qHP3uw1E2SmRSIw0oslqR+5J7/YdVqtKpTPaXOvZQM1o80ef8/YCbWWmZpSamqERgCF+LqHfHjmjLYADbezPRkTuYqAtACRGGuUeUJ5ktZltdpS5ytlxkesbUnbTgWLTeXtddZapmb0RApm0WP3pSAVs5+mnuOGwMxg3rEdMwPUYkEotFnUQaMuvbMTK7c5F/IMXBXY2GwAkRxvROj4xqW/LpkJsWOCWo/AHpTLaQg1aRLoCROWms8tHOhxiu5luJyrq0WS1I1SvRe8E52n8kVKfNg8PmZzPvlYZdIFmWI8YfLpkIl5ZNAqXDE7x93ACjhRoY+lIIt+4e1oWAODzXcUodFXKOJ+PXZv+V6twc1zNBqQ4A21HT9ef95q0s5jRFtikANT/dhej2dpx1tSPR05DFIFBqVFyFYtANzwjBi9cNxJPzB8GXQCWujwfQRAwuV8CAODHI52rfNLdVLoy2hIilMloS4lq6TseiKRA2yAfBtpa92mTMsSkTNH+yZEIMwRenzFpTVodgGv1MovzMGmKgZ+JROQe9V3JBKlbJzprkX+5pwTlbqbAF1U3QRSBUL0W8QG2qR+sshLDYdRpUG+24VRV5zYjzkc6SRoZEngXQeSsax4dqkdtkxW7C8+dySj1RpCy4AKJFGjr6ATgP9cegc0hYnK/BIzpFefLoXnEqNMisdWibVK/lgw8KdBWE4AX7/5Q1SCVben654SU1Xa6rm2gbWd+NQY88jX+tfboWfeR+rMNTI2E1lWCQ+rTtsNLGW3Scw5OD6yMNknfpEjMGprKbLZ2FEsZbSwdSeQTwzNicEFfZxm3N346/8G/gyUm7CsyQa8VcMWIdB+MkCQZsWEI0WtgsTkUW4fIgbYA7svbnU3oE4+06BCYmm1Yc7Dj6hrrDjnXIBdmB94aJJhNcVUwYZ+29lXWuzLaFNqraslo67hCi7+IoigH2rJTfdcfOiHCiH5JzoOMv7j6tEllI4f1CMx1UJwKMtpSeOCPiNzEQFuAGNojGjk9Y2FziFixrcCt+xZUu8pGxoVys85HdFqNXApgX5Ey5SPrmltKR1Lg0Wk1mOQ6rbjhHOUjrXYHNrpOMwZSfzZJ2jlKR56oaMCqnUUAgIdUkM0mkYKHEUZdm0bPseHO11Ig1n33h6p6ZTLagJZAW3ld24Dtx7mFsNpFvPbjcTRa2paVlINerco4Ds+IgSA4yx+fGbTrqnJTM07XmaERgIEpgZfRFgwcogi7m336Oqu42fn3yow2It+5Z1pfAMCKbflyBkJHPsl1ZrPNyE4OuOz9YKfRCHL5yMMKlY+UAm0xzGgLSBqNgKtGOQPaHZWPtDtE/HjEddhvQGD2ZwtW0hrxQIlJ8evZYFAhl45Uukdb4P23Lq8zo7rRCo3QUubXV84sH7nbVUZ2WKv1cSBpyWgLvDYPZRZX6UhmtBGRmxhoCyA3TegJAFj+S75bZUAKXCcZM2JZNtKXpDrXSvVpaykdyYy2QCVlqG04x2nFnfk1qDPbEBumD8iLWikoVdxORttLa47A7hAxPTtJLumnBlKftvF94tuUnIkJC9yLd19rttrRYHGWGlKiEXlHGW0bjzqDzHVmG77YXdzmZ/td75WtewREhejl05ee9ijtiPTenJUYgVCDVtHHJuDpEyUY+fN+fFehXK/S1uQebQy0kQ888cQTEAQBDzzwgPy95uZmLFmyBPHx8YiIiMD8+fNRVlbmv0H6wMSseAzrEY1mqwNv/3yyw9tZ7Q58ust5MOfqHJaN9Id+Sa4+bWVdD7Q5HCJ7tKnAPFf5yA2HT7cbzNldWIOaRiuiQnRyD1zyjYQIo1ymfONRZrWdSTq4oXSPtjJTM+wKtfFQygFXNlufxAiE6H27/mgdaBNFUS4dOSJA3w+k6jPnO9jjDyXMaCMiDzHQFkAuHZKC+HADSk3N+OFg5xfyBa4+CuzP5ltDXFkZewoUCrS5FrjMaAtcU12Btj1FtR1eEK53ZbtN6Z8ol8cLJHKg7YyMtqPldfjMtWn24Ez1ZLMBLfNy7ZiMNt+PdZU/YunIltr3Oo2gSDA/KdK5wD3d6nVQUNWIU5UtJayW/5Iv/39RFLGv6OyMNgAYmeEM6ipdPnK/6/mGBGjZSLUz2ewos9jwbYVJ8ceusdpQbXMGhjNCmClD3rVt2za8+uqrGDZsWJvvP/jgg/jiiy+wcuVKbNiwAcXFxZg3b56fRukbgiDgHlevtnd+Pom65vYPqmzIO42KegsSIgxyD1vyraGuQyuf7yrucp+2eosN0l41e0UHrqzECIzIiIHdIcrX7K2tP+Rcg0zun6jKXmdqN8W1HvnpMPu0nUkqHZmgwGE/AEiMMEIjADaHGHBBGqls5EAf9meTjOvT0qdtZ0ENTM02GHQaua9noJEy2kzNNlgV6jeqlDJXoC2ZgTYichOvwAKIUaeVN4rf23Kq0/crrHJumPeIDfXKuKh9UsPZbSer5HIrXWGSSkdygRuwkqJCMCg1CqIIuTTLmeT+bAG68SQF2k7XmWF2bWQ3Wez48+cH4BCBiwclY2iA1nHvyIIxGTj8t1m4aFBym+/HMqNNJi1wY8MNipQYljPaWpVs+clVMrV/cgT0WgG7C2vl0rpFNU2obbJCpxHQLzmizWNJfdp2KhxokzLapBPGpKyL453vE99XmhQvH3m4wZlxm27UI0LHbETynvr6eixatAivv/46YmNbMrlra2uxbNkyPPfcc5g+fTpycnLw1ltv4eeff8aWLVv8OGLvu3hQCvokhsPUbMN/t+a3e5uPXWUj545Ih54b+n4xP6cH4sINOF7RgI+2t19KsLNqXddJRp3G5xkY5J75rgzST3a0E2g7HLg9oruDya7ykT8eqYAjwLKs/KnRYkOT1VVVQ6GMNp1WIx/666jvuL9IB/0G+SHQ1rpP2xs/HQfgXAcF6ud0dKge0rK0PMBKrpa6SkemGFhtiojcE5jvuN3Y9eMyoRGATUcrcbS8/ry3b7ba8csJZw3mrKSI89yalNQnMQL9kyNgc4hY40YGYkekjLZIIz/MA5l0cntD3tmBtnJTMw6UmCAILU2xA01smB4heudbf1mtGeWmZlz72mZsPFoBg1aDhy5WVzabxKA7++OspXQkM9qkJtNKNSFPkgJtrU6RSqVyLhuahksGpwAAlrs2aaX+bP2TI2E8I3Ayqqdzc3tPYW2XT+W31lEGHSljfEwEonQaVFpt2GlqPP8d3HC40fl31T88RNHHJTrTkiVLcNlll2HmzJltvp+bmwur1drm+9nZ2cjMzMTmzZs7fDyz2QyTydTmS200GgG/murManvjpxPyoRxJVYMFaw45r3vns2yk30SG6HHvhc6eei/8cPisvqjuqGXZSNW4fFgqDFoNDpaYcMB1bdVkseO9Laewp9B5wIhZpv6R0zMWYQYtKurNOKRQ78RgIB32C9FrEKZgKXepT9u5Am31Zhse+nAX5vzzJ/lAgbftKaoBAAzz08FVqXzkN/tKAaBN//JAo9UIGODqY/fnz/dD9FLfZ3dZHSIqXJ+pzGgjIncx0BZgesSGYXq2Myvjg1/On9X26c4iVNRbkBYdgkl9E7w9PDrDpUNSAQBfuy5kuqKOGW2qIJ0Sbe+0onSSdFh6tGLNnpUmCALSop1ZbT8cLMOVL2/CnsJaxIbp8f4d45CdEjzZP3HhztcSA20tgbY4hQJtUkZbuSujze4Qsemo89DHpH4JuH5cJgDgs51FqDfbsL+o4+yyvokRiDTq0GixK9JrBnCWCy1ylUcdxIw2r9BrBFwY5/xvq3SfNimjrX8YA23kPStWrMCOHTvw+OOPn/Wz0tJSGAwGxMTEtPl+cnIySks7vuZ7/PHHER0dLX9lZGR0eNtANndEOlKjQ1BeZ8brPx5vs/n1+a4iWO0ihqRH+aU0FrVYND4TPWJDUV5nxlubTnr8OOzPph4xYQbMHJQEAHhr0wk8910eJj6xBo98ug8AcOGARDnTh3zLqNPKQY6OKp90RxVSf7ZwoyJVNSSpUS192tpzsqIBV728Cat2FmFfkQnfHej6fs351DRaUOCqNjXETwf9pL9BaZtieEZgHzh8+urhMGg1+P5AGV53ZeH522mLFSIAnQDE63kInojcw0BbALpxQk8AzrIs5zqd6HCIeGPjCQDArRf0DtiU8GA2a4gza+PHw6fRYPb8JCkAmFx9MJTon0TeM6pnLCKNOlQ1WLC3qO3mspTlNnVAkj+G1mlS+ci//O8ASmqbkZUYjk+XXCCXQw0WckZbA0tHVnop0CZltO0rqkVtkxWRIToM7xGNCX3i0SchHA0WOz7fVSxntLUXaNNoBAx3NenemV+jyPik5+sZH8aNQy+6JMG5eP+2UtmsHTnQxow28pKCggL8+te/xgcffICQEOX+zpYuXYra2lr5q6CgQLHH9iWDToM7p/QBADzz3WHMfXkTNh9zHqaQStbNH8VsNn8z6rR4+OIBAID/rD+G6gbPDhYxo01d5o10vvZW5hbipbVHUd1oRUZcKB69fBBeXjTKz6Pr3qa4ykf+xECbTO7PplDZSEnKOTLa1uWV44p/bcSRVhWiNh31fu88aW+gZ3wYosP8834q9WmTDAvgjDYAGNojGo9cPggA8OQ3edh2ssrPIwIKm51/s6lGAzQKBoeJqHtgZCYATe6bgJ7xYahrtuGzXcUd3m7D4dM4Wl6PCKMO145V54lZtctOiUSv+DCYbQ6syyvv0mNJp0mjQrjIDWR6rQYXuLJHpX5sdoeIldsL5L+BQO3PJkmNbtlUnJgVj1V3X4Ce8eF+HJF3SD3aavyU0fb57mI8sGJnl4PwSqhqkE6TKls6sqrBAovNgY2uxevErHjotBoIgoCFY51Zbcu3npIDX0PS2z9V2dKnrUaR8e07RwYdKWd6XCS0ApDX0IxTTcr1VjjcyEAbeVdubi7Ky8sxatQo6HQ66HQ6bNiwAS+99BJ0Oh2Sk5NhsVhQU1PT5n5lZWVISUnp8HGNRiOioqLafKnVTRN64TcX9Ue4QYvdhbVY+PoWXPvqZuwtqoVeK+DKEen+HiIBuGJ4GgamRqHObMPL64569Bg1DLSpytQBiXJv9pGZMfj3olFY//CFuPWC3ghjPyG/muKqfLLtRHWXyrkGk0ppDaJwtRdpPXvsdD2Ot/p6ed1R3Pb2NpiabRiVGYMXrxsBANh4tNLrpQmlQNvQDtY7vpAQYURfV0uZSKMOvVWwxr9hXCauGJ4Gu0PEvct3yFmQ/nLSFWjrFapscJiIugcG2gKQRiPghnHOrLb3Np/q8IJASq2+bkwGgzN+IgiCYuUjTSwdqRpS74P1h8vx/YEyzHrxR/z24z1otNgxrEd0QNdCB4AL+iZAEICFYzPxzm1j/XbizttiXb9Xg8UOi0253l+d9eTXh/DprmJ8sbvjAxO+0lI6UplFbmyYATqN84RfZYNZPrk7qVVvwvk5PWDQarCvyIRSUzMEAR2WGRuV6ezTtrOgWpHxtWTQBXa5FLWL0eswLtq5mP+uQpmstjqbHcVm56Zvv7DALMFL6jdjxgzs3bsXu3btkr9Gjx6NRYsWyf9fr9djzZo18n3y8vKQn5+PCRMm+HHkvqPVCLhvRj+s/+2FuGlCT+g0An454TxpPj07SbEMaeoajUbA/7vUmdX27uZTKKx2v2cmM9rURa/VYNU9E/Hdg1Ow+p4LMHtoKrQaZl0Egt4J4UiPCYXF7pDfL7u7inpl+0RLpIy27w+UYfqzG+Svp7/Ngyg617n/vXM8Lh2SglC9b3rn7S30f6ANAMa7stqGZURDo4L3BkEQ8Pi8ochKDEeZyYwHVuyC3eG/fm0nXL2ie4VyHUJE7mOgLUBdM7oHjDoNDpSYsKOdE/77i2vx87FKaDUCbp3U2/cDJNmlrvKR6w6Vo9lqP8+tO1bnKh0ZGcKTiIFuquu04s78Gix+dzsOl9UjOlSP/5s9EB/dNSHgF7tzR6Zj/2OX4PF5Q4O65GxUiB7SVPg6q62i3iz3CAuEPg1S2ZY4hcq2aDQCElwnU09WNCL3lDNANrlVr9C4cANmDW3J/OgdH45wY/vvb1Km24mKBkVOAO8rrm3zuOQ9F8e7+rRVKtOn7YirbGSyQYcY9kUgL4mMjMSQIUPafIWHhyM+Ph5DhgxBdHQ0br/9djz00ENYt24dcnNzceutt2LChAkYP368v4fvU4mRRvzlyiH4/qGpmD00BclRRvxqapa/h0WtTO2fiAl94mGxO/D890fcvr8UaONhP/VIigxB/+RIfw+DziAIgpzV9uNh/1//BwJpDaJ0RtsFfRPQPzkCUSG6Nl+p0SH4x1VD8fi8oTDqtDDqtHI5xY1HvFs+Us5o6+Hf9ceN43shOyUSN0/o5ddxuCPcqMMrN+QgVK/FxqMVuOrfm7Dg1c246t+bMOefP+GS53/Ev9a6//nmiVOujLaeITxQRETuC5gd1ieeeAKCIOCBBx6Qv9fc3IwlS5YgPj4eERERmD9/PsrKyvw3SB+KCTPgiuFpAIBX1h+Dzd42G+ONn5y92WYPTUW6q98S+cfwHtFIiw5Bo8WOnzy8eBNFEaYmV0YbsxMDXlpMKLJTnIvbEL0Gd0/Lwo+/uxCLp/RBiF7r59F1TncoLaPRCPLp7OpG3/Zpa92/b+ORCr+eygNaMtqUPE0q9Wn7355iWO0iesSGomd8WJvbXO8qHwkAg88R9EqMNCIx0ghRBA6WdO20aYPZhhMVDc7nZOlIr7vY1adtc009TDbPD5tI8lg2kgLE888/jzlz5mD+/PmYMmUKUlJSsGrVKn8Py296J4Tj34ty8MsfZmKkKwuZAoMgCPj9rGwAwKqdhThc5t7n6HZXT5zWpcWJyDNSnzY1BNoKqhrxzLd5OH66/vw39pBUOlLpHm0JEUZ89+BU7PnzJW2+Ni+dgevHZba57STXQcCfvNinrbrBgsJq5yFLfx/0G5ASiW8emIKLB3dc6joQ9U+OxN+vGgIA2FNYi60nqrAzvwb7ikzIK6trtx+fN5x0lcPvzcoaROSBgNhp3bZtG1599VUMGzaszfcffPBBfPnll1i5ciWio6Nx7733Yt68edi0aZOfRupbN0/shY93FOKHg2W4/o1f8NJ1I5ESHYKS2ia5FNniycxm8zdBEHDJkBS8tekkvt5XgosGJbv9GGabAxZXMJWnSdXh+WtHYMPh05g7Il0uXUGBJzbMgOpGqxxo8hWpdAjgLAu7p7DGrxuTLaUjlVvkJsmBthIAwOR+CRDOaBg9tncc+iZF4Gh5/XmDXoPTorA+7zQOlJiQ09Pz/1YHS0wQRSAlKkTOuiPv6RNmRL8wI440mrG20oS5yV37Oz/symgbwEAb+dj69evb/DskJAQvv/wyXn75Zf8MiMgNwzNicOngFHyzvxT/2XAMzy0Y0an77SuqxbaT1dBpBMwdyb57RF010VWi/9jpBlTUmwP2WrSktgnXvbYFRTVNeP+XU3jzljFyKXcltWS0+S87aJIr+Ln1RCWarXavHIyVDln2TgjnwekumDeqB1KiQlBeZ4ZBp4Feq4FeK8Cg1fhsz0UKtLF0JBF5wu8ZbfX19Vi0aBFef/11xMa2fLDX1tZi2bJleO655zB9+nTk5OTgrbfews8//4wtW7Z0+Hhmsxkmk6nNl1oNSY/GS9eNRLhBi60nqjD7pZ+w4fBpvP3zSdgcIsb2jsOwAO8F1V3McvVp++FAmUe9oEyuki0aAQg3qCMjqrsbmBqFX03NYpAtwMW6Aku+Lh25xxVok8qI/njYu6VKzqfSC4E2KaNNKjk1qW/iWbcRBAFPzh+KeaPScd2YjHM+3iBX/7YDxV0rQSgtdIekM5vNV6Sstu8qu37Nddh18rl/GN9biYjccfc0Z0nPz3cVo6S2qVP3eXOTs0rKZcNSkRzF912irooO1aNnnLPCQ56Xe4J5qqrBghuXbUVRTRM0AlDTaMX1r2/BukPlij9XpcJ9oj0xIDkSiZFGNFsd2JGvTD/oM7WsP1i2vqsm9k3A3JHpmD00FRcNSsa0AUmY2DcBfRIj2tyu1mqDKCpbNcZks6PK1Q6GpSOJyBN+D7QtWbIEl112GWbOnNnm+7m5ubBarW2+n52djczMTGzevLnDx3v88ccRHR0tf2VknHtjL9BdPjwNX9w3CQNTo1DVYMHNb27FW5tOAgAWT+7j38GRLKdnLBIijDA127D5eKXb9zfJ/dn0Z2WEEJHnYsP8VTqyBgBw5QhnCeCf/NinzWp3yMEwbwTaAEAQgIlZ8e3eLqdnHJ5bMAIxYed+7kFpUqCta8EaLnR97xJXn7Y1lSZYu1gmNa/RuTnM0pFERO4ZnhGDcb3jYHOIeNu1XjyX03Vm/G+3Myv91gtYJYVIKdkpzuuigyWBd+i73mzDLW9txdHyeqRGh+DbB6Zgav9ENFsduOPd7fg4t1DR56usdx6gUrJ8vbsEQZDLR3qrT5tUzWQY1x9eJ4oiFuw6ioEb9+Fgg7LlJKVstgS9DhE6HoAnIvf5NdC2YsUK7NixA48//vhZPystLYXBYEBMTEyb7ycnJ6O0tLTDx1y6dClqa2vlr4KCAqWH7XN9EiOw+p6JuGG8s9a0xeZAn4RwzMhO8vPISKLVCLhksLNk5Df7Ov777Iip2dWfLTQgqrkSBQ0puFPtw4y2MlMzykxmaATg7qnO0+U7C2rkgLqvSb+7IDhLaSolqVWgbVh6tJw96KnBac6F6aHSurP6krpDWugO5ULXZ3KiwxGn16LWZsfWWs/7fDTY7Ch0vU4YaCMict+dU5wHMZf/ko+681x3LP8lHxa7AyMzYzAiI8YHoyPqHrJTnb28DwVYRluz1Y7F72zHnsJaxIUb8N7t49AvORJv3Dwa80amw+4Q8fDK3fjPhmOKPJ/DIcrl6/1dQlMOtHmpTxsP+vmOIAjQCxo4AKyvUvY1dkLqz8aykUTkIb8F2goKCvDrX/8aH3zwAUJClNtMMRqNiIqKavMVDEL0Wvxt7lD86/qRGNsrDn+dOwQaDTOfAolUPvL7A6Wwu3miXyodyXreRMqSMtqULh1parZ2+DqXAj39kiLRLzkSfRLCYXeI+Pmo+9muSqhucL6/xITq5VKWSmid0Sb1PuiKnnFhCDNoYbY5cLyiwaPHaLTYcMzV0J2BNt/RCgJmurLaulI+8khjyynSOD0PnhARuevCAUnomxSBOrMNK7Z2fODUYnPg/V9OAWA2G5HSpIy2Q6WBk9FWVNOEJR/swObjlYgw6vDOrWPRN8lZik+v1eCZa4bjLleg/omvD+HnY10PSJmarbC51ktKVtXwhLRW2VtUi2qFe3dX1ptRVOOsyMDS9b4xLc4ZzN6gcKDtVJPzb6NnKMtGEpFn/BZoy83NRXl5OUaNGgWdTgedTocNGzbgpZdegk6nQ3JyMiwWC2pqatrcr6ysDCkpKf4ZdACYMywNH/1qAi7o2/VNTVLWuD5xiA7Vo6Legm0nq9y6r5zRxkAbkaJaMtqUyyb7cFs+Rv/1B9z13vZ2f77HdaJxaA9noGeya2Hnr/KRla6eV0ovcBMjWw7JtNefzV0ajYCBqV0rH3mg2ASHCCRHGZHEXjM+dXG8q09bRa3H/RIONzrLvzCbjYjIMxqNgMWTnYGzNzedgLWDDPEv9xbjdJ0ZyVFGzBrSfdfWRN4w0JXRdrisvktVGrrKZnfgu/2luPWtrZj05FqsOVQOg06DN24eLa9TJBqNgKWzB2LB6B4AcM5AfWdV1DuDFlEhOhh0/u1akxwVgv7JERBF4Odjyh5+lLLZ+iSEI5L7OT4x1RVo21Jbj0YFX2NS6chezGgjIg/57dNuxowZ2Lt3L3bt2iV/jR49GosWLZL/v16vx5o1a+T75OXlIT8/HxMmTPDXsIk6pNdqcPEgZ/nIT3cWuXXfo2XOkzix4bwwI1KSVCpRiYw2u0PE3788gP/3yV5Y7A6sOVSO8rqz68LvKawBAAxzLWCn9HcGoX48clrxhs2dIZVsiVe4CXlGXCh0GgFRITqM6hmjyGMOlvq0edjTQlroMpvN96bFRcIgCDjRZEFeo2f9Eg43MNBGRNRVc0emIzHSiJLaZvxvT/FZPxdFUe75feP4ntBr/d62nSioZMQ6qzRYbA6crPSsSkNXvbf5JCY9uQ53vpeLdXmnIYrOfsrv3z4O4/u031cZAG4Y3xMA8O3+UrnHs6ek/mz+LhspkQ4Gbjyq7OHHfWccsiTv6xdmRJpRD7NDxC81npetP9NJV0ZbL2a0EZGH/HZVHRkZiSFDhrT5Cg8PR3x8PIYMGYLo6GjcfvvteOihh7Bu3Trk5ubi1ltvxYQJEzB+/Hh/DZvonBaMyQAAfLqrCLWdzKCx2Bz47zbnibFLXeUniUgZUunIqi6WCKk323DXe9vx+k8nAADRoXqIIvDt/rI2txNF8aweYeP7xEOvFVBQ1YRTlY1dGocnpN9d6Yy2pMgQvH3rWHxwx3gYFWoWPciV0ba/uNaj+0v/7dkfwfcidFpcGO88XfpRSbVHjyEH2sICY0OGiEiNjDotbpnYCwDw6objZx3y2ZFfjT2FtTDoNFg4NtMPIyQKbhqNgAEpzmuigyW+79P23635eOSz/Sg1NSMu3IC7pvTBuoenYfni8RjbO+6c9x2aHo0ByZEw2xz4YvfZgXp3VEqH/SICI2gxqZ8zwKh0n7Y97A/tc4IgyFlt66uVe42dZI82IuqigD6+9vzzz2POnDmYP38+pkyZgpSUFKxatcrfwyLq0OieschOiUSz1YGVuZ0rt/D1vhKWbiHykthwKaPN8xOZRTVNuPqVn/HDQWe5lZcWjsQ907IAAN/sK2lz2+LaZlQ2WKBrVQYx3KhDTs9YAGeXj9xVUINLX/gRz36X57Vst0pX2ZY4LyxyJ/VLUPT05qC0ltKRnvz3kDLahvFEqV9cl+LcvPm4rEruyeEOqXTkAGa0ERF1yQ3jeiLMoMWh0rqzNpXfdGWzzR2RhvgAyTQhCjb+6tO26WgFHvl0HwDg7mlZ2Lx0OpbOHojeCeGdur8gCLjGVT5yZW5hl8YiZbQpXVXDU+N6tz78qFym4T5W1PCLqbHK9mlrtjtQYnbuGfRkoI2IPBRQgbb169fjhRdekP8dEhKCl19+GVVVVWhoaMCqVau6dX82CnyCIODGCc5yC+9vOQVHJzYa3/75JABg0TiWbiFSWqzco82zjDZRFHHLm1txqLQOCRFGfHjneFwxPA2zXNmnW45XtWmovddVNrJ/ciRC9C1ZXpP7OUuVbDjcstl1oqIBt729DYdK6/DPtUexdNVe2D0ITpxPS+nIwDhNei79kyOh1QiobrSi1ORe+cEGsw3HTjtLhzCjzT9mxkcjXq9DucWGtVXubSw12R1yA3KWjiQi6proMD2udVXaePSz/bjng1zcuOwXzH15E77ZVwoAuPWC3v4cIlFQk/q0HfJhRtvR8jr86v1c2Bwi5o5Iw+8uGeBR1Ym5I9Oh0wjYXVCDI2Wej1/q0RYoGW3hRh1GZkqHH5XJaquoN6O4thmCAAzm+sOnpsRFQgBwqKEZpeau92PPb7ZABBCh1SBer0y1FiLqfrirT6SwuSPSEWnU4WRlI346T1mC3QU12JlfA4OWpVuIvEEqHVnbZO1U4PtMJbXNOFJeD51GwGf3XiAvzjLjwzAoNQp2h4jvD7SUj5RKh5yZUTXV1adt87EKWO0OnK4z4+Y3t6KqwYKe8WHQCMCKbQV48MNdsCrcNN1bpSO9IUSvRd/ECADA/iL3AjUHSkxwiEBylBFJkQzU+INeI+DqZOdr5MPSKrfue6yxGSKAOL0WCXqdF0ZHRNS93D6pN7QaAccrGvDV3lL8dKQCuwpqYHeImNI/Uc68JyLltWS0+SbQVtVgwW1vb0ddsw2je8biifnDIAiCR4+VEGHEtAFJAICPu5DVVtngymgLoMzZyX0TAADr88o9WhueSaqm0SchHBFGXr/6Upxeh+GRYQCUyWqTykb2CjV6/NohImKgjUhh4UYd5uc4yy28t/nkOW/7jiub7bJhqUiMDJwLUKJgEePKaHOIgKnZ/ZNuUimQfsmRSI8JbfMzqdTr163KR+7toBn2oNQoxIUb0GCxY+PRCtz+zjbkVzUiMy4MH/9qIv51/SjotQI+312Mu9/fgWar3e2xdkRa5Koh0Aa0Kh9Z4l6graU3XozSQyI3XJfqLB/5bUUtKiy2Tt/vcKPz77R/WAgXt0RECugRG4a3bhmD314yAH+5cjCeWzAcr980GivuHI9Xb8jx9/CIgprUo62opgm1TV3PtjkXs82Ou97bLq8tXr0xp01lDU9I5SNX7SyCzcNDgFL5+oQAyWgDnGXvAeCHg+WY+MRa/OWLA8g9Ve1xCf+98iHLGKWGSG6Y5urTtkGBPm0tgbbA+XslIvVhoI3IC6TykWsOlaOgqrHd25yuM+N/e5wb9FLDciJSlkGnQbjBudCs9qBP275iZ7BnSNrZp75nDXUG2jYerYCp2QpRFFsy2s4I9mg0Aia5TlDe+8EO7CmsRWyYHm/fOgaJkUbMHpqK124cDaNOgx8OluH2d7ahyaJMsK2ldKQ6gvmDW/Vpcwf7IwSGgRGhGBYZCpsIrCrrfFZbXoOzVCjLRhIRKWdK/0QsubAvbprQC/NG9cBFg5Ixvk88Qg0si0XkTdGhevmQXp6Xs9oe+XQftp2sRmSIDm/eMlqRDLLp2UmIDzfgdJ0ZGw6fPv8d2iEF2gJpDTIiIwa3TOyFCKMOpaZmvLnpBOa/8jMueGItfvfxbqzYmo/DZXWdznaTDlmybL1/TI1r6dPm6GK/85OuEva92J+NiLqAgTYiL8hKjMCkvgkQReCDX/Lbvc1/t+bDYndgREYMhmfE+HaARN1ITBf6tO0/x+Kpb1Ik+iZFwGoXsfZgOQqqnCdWDVqNfIq1tSmu8pENFjtC9Bosu2UM+rjKJALAhdlJePvWsQg3aLHpaCXe3HTC7fG2R02lIwFn9h8A7C+pdet+LdmELIXlbwtT4wEAK0qqOn1C+DADbURERBREsl3rgUOl7h0ec8cnuYX4aHshNALw70Wj0Dfp7DWIJ/RaDeaOTAcArNzuWfnICrl0ZOCsQQRBwJ+vGIztf5yJ128ajbkj0hBu0KK4thkfbS/E71ftxcXP/4gRf/kOt7+9Dbmnqs/5eHs7aBtAvpETFYZwrQaVVhv21Td16bFOtCodSUTkKQbaiLxEymr7cFv+WWXgrHYHPvjlFABmsxF5W2y4s09bdYP7gbZ9xVKgrf3gTevykXuKagA4m58bdGd/vE7plwCdRoBGAP61cBRGufq9tTYhKx6PXjEYAPDJjkKPy5hIHA5RzuQLpEXuuUilI6XAZWc0mG04eroeAE+UBoKrkmJg1Ag40NCMvZ1c9MqBtjAG2oiIiEj9slOdQa+DJd7JaDtaXoc/froPAPDAzP6Y3C9R0ce/2tUOY82hMvngXmd9f6AMx083AMBZ5fcDQYhei4sGJeOF60Yi95GL8OYto3HPtCyM7xOHUL0WpmYb1hwqx/xXfsbDK3fjdJ25zf3tDhHf7S9FqakZgtByUJB8y6DR4IIY58HVrvZpOyVntKljzUxEgYmBNiIvmZGdhLToEFQ3WvHlnpI2P/tmXynKTGYkRDhLxhGR98TKGW3ulY4sr2tGmckMQQAGdrB4utQVaNtw+DR+Oe4sk3dmfzZJUlQI3r19LFbcOQEzByV3+Lyzh6YiRK/B8dMNcilKT9U2WWF3lT6R/jsEupgwg7wgP9jJPm0HSkwQRSAlKgRJkQzU+FuMXodLE5yvgxUl5y8faXY45FOkzGgjIiKiYJCd4lw/eCOjrclix5IPdqLJascFfeOx5MK+ij/HwNQoDEmPgtUu4rNdRZ2+39Hyejz44S4AwE0TeiIjLkzxsSkpRK/F9Oxk/O7SbKy4cwL2/vli/O++SVjg6lP3cW4hpj+zHm9uPIEjZXV48ptDmPjEGtz5Xi4AZ5At3Kjz56/QrUnlI9d3IdBmF0UUNLN0JBF1HQNtRF6i02pw/bhMAMCbm07gm32l+CS3EO9uPomX1x0FACwal9lu5gsRKUcKMNW4WTpyv6tHWFZiBMIM7S+eBqVGITMuDM1WBz7aXgDg7P5srU3MSsDY3nHnfN4Iow4XD3IG8Fbv7Pyitj2VrtOnkSE6Vb3XSIHNzvZpk8q2MJstcFyX4vw7X11WDbPDcc7bHm80wwEgWqdFcgevNSIiIiI1GejKaMsr7XzPr8768+f7kVdWh4QII164diS0GkHRx5dck5MBAFixtQBW+7mv5wDnIb87392OerMNY3vH4ZE5g7wyLm/SaTUYkh6Np64ejlX3TMTQ9GjUmW34y/8O4KLnf8Qr64+hzGRGTJget0zshVcW5fh7yN3ahXHOdePW2gY02D3rcV7UbIFVFGEQBKQa9UoOj4i6GfXsuhGp0LVjMqHXCthfbMKv3s/Fb1buxp8+249DpXXQaQQscgXiiMh7YsNcpSPdDbS5en4NTuu4FIggCHL5SLPNufjsKKPNHVe5eiJ8sbu4U4vajki/s1r6s0mk/+YHOpnRJvdnY6AtYEyJi0SaUY9qmx3fVpx7HvNalY0UBO9sFBERERH5Uq/4cBh0GjRa7CioblTscT/dWYQPtxdAEICXrhuBxEjvZeBc6ephlldWhz+u3nfOsvZ2h4gHVuzE8YoGpEWH4N+LRkGvVfeW46jMWHy65AI8Pm8oYsP00GoEzMhOwiuLRuGXP8zAn68YjMz4wM7YC3a9Qw3ICDHAKorYXNPg0WOcdJWNzAw1QMu1CBF1gbo/9YgCXGKkEb+7JBvZKZEYlRmDyf0SMGtICq7O6YGXFo5EUhRLZBF5W4yHpSP3FTmDA0PSzh28kcpHAoBRp0G/pAg3R3i2Sf0SEB9uQGWDBRuPVHj8OJX16gy0SX3a9nc2o62IjcgDjVYQcI0rq+3doopzbswcbnQF2sJZqoWIiIiCg06rQf9k57pAqT5tm49V4g+r9wIA7p/eDxP7JijyuB2JCTPgxetGQiMAH24vwD/XHu3wts9/fxjr8k7DqNPg1RtHIyEiOK7rtBoBC8dmYuv/zcTuRy/GslvGYNbQVBh1Wn8PjeA8+DrNVT5yQ5VnZVpPukrYs2wkEXUVA21EXrZ4Sh9888AUrLrnArx3+zi8ckMOnrlmOHuzEfmIlNHmdunIEldGW/q5m1sP7xGD1Ghn0HxwWhR0Cpzc1Gs1uHx4GoCulY+UGpfHqy3Q5iodebS8DhbbuTP6Gsw2HDtdD4ClIwPNdSlx0ArAxpp6PHK0qN1gW7XVhq9PO19r7M9GREREwUSpPm21TVYsXbUHC1/fgkaLHRP6xOP+Gf2UGOJ5zRyUjMeuHAIAeO77w/g4t7DNzxvMNvxzzRH8y9Ue44n5QxWp8BFo9FoNItiLLSBNje1anzYpo61XqLrWzEQUeBhoIyKioBbrCjJVN3Q+o6220YqCqiYAwODzZLRpNAIucwXOR/c6d/81d0jlI787UIp6s82jx6hqcJ7OU1tGW4/YUESF6GC1izhSfu4F04ESE0QRSIkK8WrpHHJf7zAjnhng7O3xRmEF/n68pE2wrbjZgit3HMXBhmbE6LSYlRB8mzJERETUfWWnOAMAh7qQ0fbNvhLMfG4D/rvV2Q/6+nGZeO2mHK/1ZWvPjeN74ldTswAAv/9kD346chqmZiv+tfYIJj25Fs9+fxgAcMek3rhqZA+fjYsIACbFRkAvCDjSaMbmmnq378+MNiJSCo9jEBFRUGspHdn5jLb9xc4Mm8y4MESHnr8h8m8uHoCspAhcNky5TNVhPaLRJyEcxysa8M2+Ulyd4/6itbJBKh2prkWDIAgYlBaFLcersL/YdM5g555C51wxmy0wLUyNh9kh4veHC/Gv/HKEaDR4uHcKjjQ047rdx1BktiLVqMd/h/dBJhe3REREFEQGpnqW0SaKIraeqMJrPx7HmkPlAIA+CeF4fN5QjOsTr/g4O+N3lwxAcU0TPt9djF+9lwutRoCp2XkYsHdCOO6ZloX5oxhkI9+L0euwMDUO7xZX4qkTJVg1oq9bfZ+lQFvPEHUdTiWiwMNAGxERBbU4DwJt+4ql4M25y0ZKQg1aLByb6f7gzkEQBFw1Mh3Pfn8Yn+4s8ijQptbSkYCzN96W41V4Zf0xjOkVh94J4e3ebh/7swW8W9ITYHY48OjRYjxzshQVVhs+L69GldWOvmFG/Hd4FjK4sCUiIqIgI2W0napqRIPZhvDzlB5sMNuwemcR3t9yCodKnVlwOo2AX03Nwr3T+yJE77++YBqNgKevGYbyumZsOV4FAOibFIH7pvfFZUNTFSmfT+SpB3omY0VJFTbXNGBTTT0mucpJno8oijjZ7Fwz9w7joT8i6hoG2oiIKKjFuHq0VTdaIYpip0637Stynjo9X9lIb7tyhDPQtulYBcpMzUiO6nwPq2arXQ5CxUeoL4hx88Re+HpfKU5UNGDuy5vwyg2jMDHr7Ibve12/41BmtAW0uzKSYHaI+MfxErxdVAEAGBEZhg+G9UG8gZejREREFHziI4xIjDTidJ0ZeWV1GJUZ2+7timqasOynE/hoe4FcMj5Ur8XckWm47YLe6JfcuaCBtxl1Wrx642i88dNxDEyNwqWDU6DxYQlLoo6khRhwY1o8lhVV4MnjpbhgVESn1v2nLTY02h0QAB78I6Iu45ETIiIKalKPNovNgSarvVP3kTLaBqd1LqPNWzLjwzC6ZyxEEfhsV5Fb9/3L/w7g2OkGxIbpMblfopdG6D0ZcWFYvWQiRmbGoLbJipuWbcV/t+YDAOqarXh/yynM+edPOFrurMPP0pGB7/6eyfhNr2QAwLTYSHw8IotBNiIiIgpq5+rTlldah4c+3IWpT63Dm5tOoN5sQ5+EcPxpziBs+cMMPD5vTz0i8wAANL5JREFUWMAE2STRoXr85uIBmD00lUE2Cij390xGiEbANlMD1ld1ri+iVDYyzaiHUcMtciLqGu5uEBFRUAs3aKHXCrDaRVQ3WhF2no39erMNJyoaAPg/ow0A5o5Mx/ZT1Vi9sxh3Tsnq1H1W7SjE8l/yIQjA89eOQGKkOstgJEWG4L+Lx+N3H+/B57uLsXTVXny+qxi7CmrkoKlBq8GNE3qq9nfsbn7bOxW3pCcgQa9zq3cCERERkRoNTI3CT0cq8L89xag3W9FkcR7+O1Rqwvq80/LtJmbF466pWZjcN4EBLCIPJBv1uDk9Aa8WnMaTJ0oxLS7yvOsNqWxkL/aKJiIFMNBGRERBTRAExIQZcLrOjOoGC9JjQs95+4MlJogikBIVEhDBmznDUvHYF/txsMSEz3YV4coR6ee8fV5pHf6wei8A4L7p/TBtQJIvhuk1IXotXrxuBLISI/D8D4ex+XglAGdPiOvGZGDeqB6IU2EPuu4s0aD39xCIiIiIfGJgqjMj7edjlfj5WGWbnwkCMGtICu6akoXhGTF+GB1RcLk3MwnvFlViV10jvq804eKEcx+clTLaejPQRkQKYKCNiIiCXmyYHqfrzKhptJ73tlJfsyHp/i0bKYkJM+COyX3wyvpj+O3He5AZF4aRHfR3qGu24u73c9FsdWByvwT8ekY/H4/WOwRBwK9n9sOgtChsOlqBy4alYnTPWGZEEREREVFAu3RwKraOrUJNoxWhei1CDVqE6rWICtXj8uFp6J0Q7u8hEgWNRIMet/dIwL/yy/H0iVJcFB91zjXjySZnRlvPUB7cJKKuYwFaIiIKejFhzgvnqkbLeW+7v9gEIDDKRkoevngAZg5MgsXmwOJ3c1Fc03TWbURRxO8/2YvjFQ1IjQ7BC9eOgDbIys5cNCgZf75iMMb0imOQjYi65JVXXsGwYcMQFRWFqKgoTJgwAV9//bX88+bmZixZsgTx8fGIiIjA/PnzUVZW5scRExGRGoUatHh83jC8ckMOnrt2BP5+1VD8cc4g3D+jH4NsRF5wT2YSwrUa7K1vwtcVtee8rZTRxtKRRKQEBtqIiCjoxbkCbTWdCLS1ZLQFTqBNqxHwwnUjkZ0SiYp6M25/ZzsazDb557sKanDTm1vx5d4S6DQC/nX9KMRHcLFARNSRHj164IknnkBubi62b9+O6dOn48orr8T+/fsBAA8++CC++OILrFy5Ehs2bEBxcTHmzZvn51ETERER0bnE6XW4s0ciAOAvx4rRYLe3e7tdpkbsMjUCAAaEh/hsfEQUvBhoIyKioBcb7uwJVd1w7tKRzVY7jpTXAwic0pGSCKMOb9w8GgkRBhwsMeHBD3dhb2Etbn97G+a+vAk/HamATiPgr3OHIKdn+6UliYjI6fLLL8fs2bPRr18/9O/fH3//+98RERGBLVu2oLa2FsuWLcNzzz2H6dOnIycnB2+99RZ+/vlnbNmyxd9DJyIiIqJzuDszCWlGPU42WfD3YyVn/dzicODBQ/lwAJibFIP+DLQRkQIYaCMioqAnlY6sPk9GW15pHewOEfHhBqREBd7Fdo/YMLx642gYdBp8d6AMl/9rI9YcKodGAK7O6YG1v5mGhWMz/T1MIiJVsdvtWLFiBRoaGjBhwgTk5ubCarVi5syZ8m2ys7ORmZmJzZs3d/g4ZrMZJpOpzRcRERER+VaUTovnsjMAAG8WVWBjdV2bn/8rvxwHG5oRp9fib/16+GOIRBSEGGgjIqKgFxvmzGg7X+nIfcXOspGD0s7dNNmfcnrG4qn5wwAAggBcOSINPzw0Fc9cMxyZ8WF+Hh0RkXrs3bsXERERMBqN+NWvfoXVq1dj0KBBKC0thcFgQExMTJvbJycno7S0tMPHe/zxxxEdHS1/ZWRkePk3ICIiIqL2TIuLwk1p8QCABw8VoN7mLCF5qKEJz5909t39e78eSDDo/DZGIgoufDchIqKg15LR1nHpyCNldfgktxBAYPVna8/ckenolRCOqBAd+iRG+Hs4RESqNGDAAOzatQu1tbX4+OOPcfPNN2PDhg0eP97SpUvx0EMPyf82mUwMthERERH5yZ+y0rCuqg4FzRb85VgxHu/fAw8dKoBVFHFRfBTmJsX4e4hEFEQYaCMioqAX6wq0tZfRtr+4Fv9aexTf7C+FKAJajYBLBqf4eohuG5ER4+8hEBGpmsFgQN++fQEAOTk52LZtG1588UVce+21sFgsqKmpaZPVVlZWhpSUjj8fjEYjjEajt4dNRERERJ0QodPi+ewMXL3rGN4trkS11Y4dpkZEajV4akCPgK1iQ0TqxEAbEREFPal0ZH5VI577Lg+NFjsarXYUVDXipyMV8u0uHZyCe6f3DfiMNiIiUp7D4YDZbEZOTg70ej3WrFmD+fPnAwDy8vKQn5+PCRMm+HmURERERNRZk2IjcXt6ApYVVeCL0zUAgEf7piPVaPDvwIgo6DDQRkREQS8pMgSAs3TkS2uPtvmZIABzhqXh3gv7YkBKpD+GR0REPrZ06VLMmjULmZmZqKurw/Lly7F+/Xp8++23iI6Oxu23346HHnoIcXFxiIqKwn333YcJEyZg/Pjx/h46EREREbnhD1mpWFtlwokmCy6IicCi1Dh/D4mIghADbUREFPQy48Pwf7MHIq+sDmEGLUINWoTqtYgw6nBhdhKy2OeMiKhbKS8vx0033YSSkhJER0dj2LBh+Pbbb3HRRRcBAJ5//nloNBrMnz8fZrMZl1xyCf7973/7edRERERE5K5wrRZvD+2D94orcG9mMktGEpFXCKIoiv568ldeeQWvvPIKTp48CQAYPHgw/vSnP2HWrFkAgObmZvzmN7/BihUr2ixwk5OTO/0cJpMJ0dHRqK2tRVRUlDd+DSIiIiIi6oTudG3enX5XIiIiIqJAxmtz8jaNP5+8R48eeOKJJ5Cbm4vt27dj+vTpuPLKK7F//34AwIMPPogvvvgCK1euxIYNG1BcXIx58+b5c8hEREREREREREREREREAPyc0daeuLg4PP3007j66quRmJiI5cuX4+qrrwYAHDp0CAMHDsTmzZs73R+B0WoiIiIiosDQna7Nu9PvSkREREQUyHhtTt7m14y21ux2O1asWIGGhgZMmDABubm5sFqtmDlzpnyb7OxsZGZmYvPmzR0+jtlshslkavNFREREREREREREREREpDS/B9r27t2LiIgIGI1G/OpXv8Lq1asxaNAglJaWwmAwICYmps3tk5OTUVpa2uHjPf7444iOjpa/MjIyvPwbEBERERERERERERERUXfk90DbgAEDsGvXLvzyyy+4++67cfPNN+PAgQMeP97SpUtRW1srfxUUFCg4WiIiIiIiIiIiIiIiIiInnb8HYDAY0LdvXwBATk4Otm3bhhdffBHXXnstLBYLampq2mS1lZWVISUlpcPHMxqNMBqN3h42ERERERERERERERERdXN+z2g7k8PhgNlsRk5ODvR6PdasWSP/LC8vD/n5+ZgwYYIfR0hERERERERERERERETk54y2pUuXYtasWcjMzERdXR2WL1+O9evX49tvv0V0dDRuv/12PPTQQ4iLi0NUVBTuu+8+TJgwAePHj+/0c4iiCAAwmUze+jWIiIiIiKgTpGty6Ro9mHEdQkREREQUGLrTOoT8w6+BtvLyctx0000oKSlBdHQ0hg0bhm+//RYXXXQRAOD555+HRqPB/PnzYTabcckll+Df//63W89RV1cHAMjIyFB8/ERERERE5L66ujpER0f7exhexXUIEREREVFg6Q7rEPIPQQzyMK7D4UBxcTEiIyMhCILfxmEymZCRkYGCggJERUX5bRzkOc6h+nEOgwPnUf04h+rHOQwO/phHURRRV1eHtLQ0aDQBV8VeUYGwDuFrNThwHtWPcxgcOI/qxzlUP85hcOA6hIKRXzPafEGj0aBHjx7+HoYsKiqKHwQqxzlUP85hcOA8qh/nUP04h8HB1/PYXU6QBtI6hK/V4MB5VD/OYXDgPKof51D9OIfBgesQCiYM3xIRERERERERERERERF5gIE2IiIiIiIiIiIiIiIiIg8w0OYjRqMRjz76KIxGo7+HQh7iHKof5zA4cB7Vj3OofpzD4MB5DH6c4+DAeVQ/zmFw4DyqH+dQ/TiHwYHzSMFIEEVR9PcgiIiIiIiIiIiIiIiIiNSGGW1EREREREREREREREREHmCgjYiIiIiIiIiIiIiIiMgDDLQREREREREREREREREReYCBNiIiIiIiIiIiIiIiIiIPMNBGRERERERERERERERE5AEG2hSydetW1NTUAABEUfTvYIiIiIiIqFvgOoSIiIiIiMi/GGjroqKiIixYsADjx4/HU089BQAQBMHPoyJ3lJaW4g9/+AOee+45fPHFFwC4SaE2ZWVlePvtt7Fx40ZUV1cD4ByqUWlpKd555x389NNPMJlMADiPalNdXY1Tp04BAOx2u59HQ9R9lZeXY8WKFdi0aRMqKyv9PRzyEq5D1I/rEPXjOiQ4cB2iflyHEAUGrkOoO2OgrQt+85vfIDMzE2azGdnZ2QgNDfX3kMhNjz/+OPr164fdu3dj9erVuPLKK7F27VoIgsALa5V47LHH0KdPH7z//vu49tprsWDBAmzbto0bTSrzxBNPoFevXnjttddw6aWX4vrrr8fu3bv5WlSRJ554ApmZmfi///s/AIBWq/XziMgTFRUVyMvLA8BNCrX6v//7P2RlZeHVV1/FxRdfjMWLF+PgwYP+HhYpjOsQ9eM6RP24DgkOXIeoH9chwYHrEPXjOoS6OwbaPLBx40ZERkZizZo1WL9+PT777DOMHj0aa9euBcCTT2qxdetWfPTRR3j//ffx5ZdfYtWqVbjsssvw5ptvAuCJYDX44Ycf8NVXX2H16tX44YcfsHr1asTHx2PhwoUoLS319/Cok06dOoUPPvgAy5Ytw6ZNm7By5UoIgoAFCxbAbDbztRjgzGYzHnjgAaxatQqTJ0/GqVOnsHr1agCAw+Hw8+jIHU8//TRSU1Nxyy23AHBuUvCaRj1qa2tx991344cffsDnn3+Ob7/9Fu+99x4aGhrw+uuv+3t4pBCuQ4ID1yHqx3VIcOA6RN24DgkeXIeoG9chRE4MtHVS6zf4hoYGvPvuu9i1axcmT54Mh8OBIUOGoKKiAmVlZbwYC1Bnfkh/8803qKmpwZVXXgkASExMhF6vx/XXX9/hfci/pPmQ/nflypXQaDS4+OKL4XA4MHbsWEyfPh3Hjx/H888/j6amJn8Olzrpu+++Q1lZGa655hoAwOzZs/HCCy/AZDLhD3/4g59HR+ciiiKMRiOysrKwePFiPPnkk4iPj8f7778Pk8kEjUbD91EVsNlseOSRR/DJJ59g4cKFaGpqwquvvgqAn4NqUllZCYfDgYcffhgXXnghDAYD5s2bh+joaDQ3NwPgfKoV1yHqx3WI+nEdEpy4DlEvrkOCA9chwYHrECInBto6wWw2o7GxUf73xRdfjKuuugqA85SMRqNBREQEamtrmaIeoM6cQwAYNmwYTp06heXLlyMvLw8LFy7EV199hX/84x+YPXs2jh07xs2KANJ6DgVBgNVqRWRkJNLS0lBfXw+Nxvl21tzcjJycHLz00ktyjXYKHI2NjSgoKIDFYpG/16NHD9jtdpw+fRqA8301KysLf//73/Hyyy9zHgNM6zmU3iPvuusuLF68GEOHDsVll12GoqIivP322/4dKHWKKIrQ6XTIysrCTTfdhEcffRQTJ07Ea6+9hsrKSmg0Gp4IDlANDQ0oKCiQS+skJCTgvvvukzcLpXmLjIyU33N5XaM+XIeoH9ch6sd1SHDgOkT9uA4JLlyHqBfXIUTtY6DtPP76179i6tSpuPzyy/Hb3/4WJSUlEARBfjOR3ihmzpyJkpISHD16FAAj9YHkzDksLi4GAEybNg0PPfQQPv74Y4wZMwalpaX4/vvv8dBDD6Gurg433nij3AiZ/OvMOSwqKoJer0dWVhaKioqwZMkSHD9+HI888giWLl2KRx55BD179sRrr70GgK/HQPHYY49h5MiRmD9/PmbMmIFDhw4BAAwGA/r374+PPvoIAOTNigULFiArKwvPPvssAJb/CARnzqFUQ7/1idFrrrkGAwYMwBdffIEjR45AEATOXYCRFkYOh0O+jrn55ptxzz33ICsrC1dccQV0Oh2efPJJAFwUBaLHHnsMffv2xWWXXYZp06Zhy5YtiIqKwpAhQwC0BGAAZ6nBiRMnAmC/C7XhOkT9uA5RP65DggPXIerHdUhw4DpE/bgOIeoYA20d2Lt3LyZOnIgPP/wQv/71rzFixAisW7cO999/P4CW5qrSm77UiHzfvn1tvk/+c745jImJwTPPPIOHH34Yw4cPxzvvvIPJkydj3rx5+OCDD7BlyxaeYPOzjubwvvvuAwAsXrwYN998M3bs2IHJkyfjk08+wWeffYYrrrgCkyZNgslkgiiKfD362ebNmzFmzBh8/PHHeOqpp3D//fdDo9HgjjvuAABMmTIFPXr0wLp167B//34Azk2JiIgIzJ07FwcOHEBTU5N8sUa+d7451Ol0csP4mJgYXH311WhqapJ7zbQ+jcgNJ/9qvTCaOnUqfvnlFwBosxExZcoUXHrppfjiiy+wa9cublIEmGeffRbLly/HsmXL8Mc//hEJCQm4+uqr8fXXX8u3kTadjhw5gqamJowdOxYAmPGkElyHqB/XIerHdUhw4DpE/bgOCR5ch6gf1yFE58arhXZYrVasXr0aqamp2LBhAxYuXIjnnnsO9957L06cOIH8/Pyz7jNo0CDU1NSgpqYGACP1/nauOTx58mSbOTx06BAaGhqQmZkpf2/v3r1IT09vU1aCfOtcc5ifn48TJ05Ap9Ph7rvvxqZNm7BmzRocOHAAM2fOBABs2bIF0dHRXNwGgE2bNmHYsGHYtGkTrrzyStxwww2YNWsW4uLi0NzcDL1ejxtvvBElJSVymQ9p3o4ePYrQ0FCEhoZyYeRHHc1hbGwsbDYbgLYnfS+//HKMGzcOmzZtwtq1a/HRRx9hyZIlALgB7E/tLYzmz58vL4ykjYiwsDDMmTMHGRkZ8mlSjUYjzzX5h/QeuH79ekyePBmzZ8/GggULsHr1avTr1w///ve/sWPHDgCQTwkfPHgQCQkJ8gnT77//Hn/961/99jvQ+XEdon5ch6gf1yHBg+sQ9eM6JDhwHaJuXIcQdQ4Dbe0QRRGDBw/GPffcg/j4ePlD22AwoKKiArGxsW1u73A4oNVqMXXqVKxZswYAI/X+5s4ciqIIu92OV199FXV1dTh27Bj++c9/Yvz48Rg8eLC/foVu73xzmJCQIN82KioK2dnZ8r+//fZbRERE4MYbb/T5uKmFdDF2zz334He/+x2ioqIAOBse//DDD+jbty+2bdsGALjiiitw5ZVX4tNPP8Uf//hH5Ofn4+DBgzh16hRmzJgBgAsjf+jMHG7ZsgWAcwHUuqTZ9ddfj6amJsyZMwc33HADwsPD/fNLkFsLI+m2Y8aMwRVXXIHdu3dj1apVWLVqFW699VZYrVa//R7dnSAIaGhowJEjRzBixAgAkDfi//rXvyI/Px9ffPEFbDabfPL+s88+w9SpU1FZWYlZs2Zh9uzZPBUc4LgOUT+uQ9SP6xD14zpE/bgOCQ5chwQHrkOIOoeBtnYYDAbMmzdPvqiS1NbWIiMjAyEhIW2+L72J1NXVwWKxoLq62mdjpfZ1Zg6li7CLLroIM2bMwN13343Zs2dj9OjRiI2NxbJly86aa/Idd1+HDQ0NWL58Oe655x7MmzcPEyZMkE/OkH9IC9KwsDAMGDAAAPD5558jLi4O5eXl2LNnD6677josXLgQNpsN999/P5YuXYqXXnoJc+bMwdixY9GrVy/ceeed/vw1urXOzOG1116LG264AbW1tQCcG7xFRUV4/fXXkZubi4ULF6KsrAzPPPOM336P7s6dhZFWq5UXQLNnz0Z6ejquvvpqXHfddUhJSYFer/fXr9HtORwOhIeHY9SoUfjvf/8LwFkuCQAmTZqE8ePHY82aNSgoKAAAVFdX48cff8Tq1auRlpYGvV6P0tJSPProo377Hej8uA5RP65D1I/rEPXjOkT9uA4JDlyHBAeuQ4g6R+fvAQSijmqp//zzz8jJyYFer2/T3NFms0Gn02HJkiVITU0966Qp+V5n5xAAMjMz8Y9//ANz585FYWEhhgwZgmHDhvl6yHQGd1+HYWFhKCkpwZEjR7B27VqMGzfO10Omdpw5jzabDR988AFmz54NURRx4MABjBgxAosXL8b06dNx22234aKLLkJ+fj5iYmJ4mjsAdHYOb7/9dlx44YUAnKfXNmzYgC1btsg12cl/zlwY3Xvvve0ujG666Sb07t0bGo0GZWVlePrpp7FmzRrcdtttePrpp3l9EyBuvPFGzJ07Fz/88ANmzpyJ5uZmhISE4De/+Q0GDRqEyspK9O7dG3V1dRBFESkpKVi5ciXGjBnj76FTJ3Adon5ch6gf1yHBgesQ9eM6RP24DgkuXIcQnYfYDRUXF4u7d+8WT58+fdbPrFZrm3/b7XZRFEXRZrOJmZmZ4urVq+WfHTt2TP4Z+ZZSc3j06FGvjpM6pvTrUBRF0WKxeGew1CF35rE99fX1Ynx8vPjss896Y3jUCZzD4Ce9h3711VeiwWAQv//+e1EURbGpqUkURVHMy8sTtVqtuG3bNvk+K1asECdNmiRu2bLF9wPupurr60Wz2dzuz1q/FsvKysT58+eLAwYMaHObqqoqMSUlRXzzzTdFURTFxsZGcdeuXd4bMHmE6xD14zpE/bgOCQ68hlU/zmHw4zpEHbgOIVJGtysd+cADD2DAgAFYtGgRhgwZgk8++QR1dXUAnKdldDodRFHEI488AqClHMumTZvgcDgwY8YMFBUVYcGCBejbty9KSkrYB8HHlJzDfv36oaSkxG+/S3fljdchAJYS8DF357E9//vf/9CnTx/Mnz/fV8OmVjiHwaGhoUEuwXKm1nXyc3JycPnll+Pee+8FALn0VWJiIhITE7Fv3z75ftdeey1++uknnsr3kYcffhgTJkzAzp072/25TqeDw+HAI488gubmZvzud79DVVUV7rvvPpSWlgIANm/ejNjYWLnUWWhoKIYPH+6z34HOj+sQ9eM6RP24DgkOvIZVP85hcOA6RP24DiFSTrcKtL355ptYt24dvvjiC6xYsQJXXHEFHnnkEbz00ksAnLWD33jjDaSnp+Ojjz7CqVOn5PseOHAAffr0wYsvvoh+/fqhrq4OJ06cQGpqqr9+nW6Jc6h+nMPg0JV5LC4uRn5+Pv785z/jgQcewJw5c5Ceni43Pybf4BwGByUXRtOnT/fl0AlAYWEhFixYgPXr1yMvLw/vv/8+6uvrz7rdm2++idTUVKxcuRIOhwNjx47FO++8g48//hiTJ0/GwoULcc0112DGjBlITU3lazEA8fpH/TiH6sc5DA68hlU/zmFw4DpE3bgOIfICX6XOBYK5c+eKV155ZZvv/fa3vxWHDRsmbtiwQczLyxMvvPBC8Y033jirDMucOXNEQRDEIUOGiN9++60PR02tcQ7Vj3MYHDydx8LCQvGJJ54Q+/XrJw4dOlRcu3atj0dOEs6huhUUFIjXXHONmJOTIxoMBvHee+8V6+rqzrrdsmXLxKSkJHHAgAHiiRMnRFF0lm5JSUkR+/btK1533XViWFiYeO+994oWi0V0OBw+/k26t71794q//vWvxa1bt4rLly8X9Xq9XFJHsm/fPvH6668XX3vtNfm1KM3T9u3bxVdffVW8//77xXXr1vl6+OQGXv+oH+dQ/TiHwYHXsOrHOVQ3rkOCA9chRMoTRDG4Q82iq3lqU1MTbrjhBvTq1QvPPvus/PM9e/bgD3/4A0JCQvDxxx/DarWeVfbBZrNh2bJliIiIwKJFi3z9K3R7nEP14xwGByXm0W63Y+/evSgrK8Mll1zi61+h2+McBo99+/bhjTfewKJFi3D06FHcfPPN+OqrrzBz5kz5Nvv378c//vEPTJs2Dbfddhu0Wq38N5Cbm4vc3Fzs378fV111FaZNm+a/X6Ybcjgc0Gg0aGpqQkVFBTIyMgAA48ePR3R0NN577z0kJSXJtzebzTAajfK/pXmkwMbrH/XjHKof5zA48BpW/TiHwYPrEHXjOoTIi3wf2/O+ZcuWid999538bynafuedd4qjRo06q9Hqiy++KI4ZM0Zcs2aNT8dJHeMcqh/nMDhwHtWPcxhcpIbijY2NYn5+vvz9cePGiRdffLFYVlbW5vbNzc1t/s2Tov5z5muxNemE6O7du0VBEMS33377rGyKjnBOAwvfc9WPc6h+nMPgwHlUP85hcOE6RL24DiHyjaAKtG3cuFEcNWqUKAiCeNttt4nFxcWiKIqi1WoVRVEUT5w4IWq1WvHdd99tc7/Dhw+LGRkZ4qeffurzMVNbnEP14xwGB86j+nEOgwcXRurW0WvxzP/+0ubFjTfeKGZnZ4vHjh0767E4Z4GL77nqxzlUP85hcOA8qh/nMHhwHaJuXIcQ+ZbG3xl1SqmpqcGHH36I0aNH4+9//zvWr1+P9evXA2hpwNmrVy8sXrwYf/rTn3D48GH5vv369UNDQwOKior8NHoCOIfBgHMYHDiP6sc5DA6bNm1CTk4O7rjjDqxYsQIlJSUA0KbBtFarhcPhwLBhw3DDDTfgiSeeaNMwXiK2UymcJT+871yvxY7++7/yyis4deoUli9fjpqaGqxevRqffvrpOe9D/sX3XPXjHKof5zA4cB7Vj3MYHLgOUT+uQ4j8wL9xPuWYzWZxzZo14o4dO0RRFMWLL75YvPzyy8VDhw6JotgSeW9ubhZ79OghXnfddeJPP/0kiqIofvnll+LQoUPFAwcO+GfwJIoi5zAYcA6DA+dR/TiH6lddXS3ed9994p133in+4x//EPv06SMuX7683dtKJxDr6+vF0NBQ8a9//atYXV0trlq1Sly9erUPR01n6uxrUSKdAn7qqafEsLAwsX///mJISIi4cuVK3w6c3ML3XPXjHKof5zA4cB7Vj3OoflyHBAeuQ4h8L2gCbWf6+eefxbS0NPGFF14QzWazKIqiaLFYRFEUxfXr14uzZs0Sw8LCxEsuuUQ0GAzigw8+KH9AUGDgHKof5zA4cB7Vj3OoPlwYBaf2XotnOnbsmHjLLbeIgiCIv/rVr8S6ujofj5K6iu+56sc5VD/OYXDgPKof51B9uA4JTlyHEHmfIIrt5PCqnMPhgEajweLFi7F371688MILGD9+fJvbVFVVYdOmTTh+/DhmzJiBIUOG+Gm01B7OofpxDoMD51H9OIfBYfPmzbj66qvxu9/9DnfffTcMBsNZtzl+/Dj++te/4p133sFdd92Fp59+GhEREX4YLbWnM6/Furo63HbbbdixYwc+++wzvhZViO+56sc5VD/OYXDgPKof5zA4cB2iflyHEPmIvyN9nXX48GHx6aeflk9QtCY1VD3z38XFxWLv3r3F3//+92Jtba0oiqJ45MgR7w+W2sU5VD/OYXDgPKof57B7kU713nHHHeK4cePEzZs3n3Ubk8kkXn311WKfPn3EvXv3+nqI3ZZSr8WjR4/Kt5GalFPg4Huu+nEO1Y9zGBw4j+rHOexeuA4JXFyHEAUejb8Dfedjt9uxZMkSDB06FAcPHsTp06flnzkcDgDOhqpWqxWbNm2S/22325Gamoq77roLX3zxBd544w1cdNFFuO2229DQ0OCX36W74hyqH+cwOHAe1Y9zGDyOHDmCZ555Bnl5eWf9zGaztfm3NLd/+ctfUF5ejs8++wwmkwkAcOzYMQBAaGgoXnrpJRw7doynD31A6dfirbfeivr6euh0OqSmpvrld6Kz8T1X/TiH6sc5DA6cR/XjHAYPrkPUjesQogDm70jf+Tz11FPiBRdcIG7ZsqXN91vXBH7xxRfFyMhIcenSpWJjY2Obn+fn54s6nU4UBEGcO3euePr0ad8NnkRR5BwGA85hcOA8qh/nUP1sNpt4zz33iEajUbztttvk5u+iKLbpR2GxWMSNGze2uZ8oiuITTzwhDh48WHz22WfFmTNnipMnT2btfD/ga7F74DyrH+dQ/TiHwYHzqH6cQ/XjOiQ48LVIFLgCNtDmcDjE+vp6ccKECeLrr78uiqKzceOrr74q/vTTT/Kb+W9/+1sxNjZWfP/9989qyLly5UpREARxzJgxchNP8h3OofpxDoMD51H9OIfBgwsjdeNrsXvgPKsf51D9OIfBgfOofpzD4MF1iLrxtUgU+AI20CaKznqzycnJYkFBgfjQQw+Jqamp4sSJE8WkpCRx+vTpYkNDg3j69GnRZDK1e/9t27aJr776qo9HTa1xDtWPcxgcOI/qxzlUNy6Mggdfi90D51n9OIfqxzkMDpxH9eMcqhvXIcGDr0WiwBYwgbZffvlFFMW26cqNjY3i4MGDxZtvvlmcP3++eODAAbGyslLcu3evGBMTI/7mN785682f/IdzqH6cw+DAeVQ/zmFw4sJIffha7B44z+rHOVQ/zmFw4DyqH+cwOHEdoj58LRKpj98DbatXrxbT0tLEuLg48cSJE6IottT/raqqEu+44w4xMjJSnDdvnmi32+U3mDfffFOMjo6WU5nJfziH6sc5DA6cR/XjHAYPLozUja/F7oHzrH6cQ/XjHAYHzqP6cQ6DB9ch6sbXIpF6aeBHH3zwAf7xj39gypQpGDRoEJ544gkAgFarBQDExsZi+vTpMBgMsNvt0Gg0EEURADBo0CAYDAYcPHjQb+MnzmEw4BwGB86j+nEOg8Onn36K9PR0zJo1CydPnoRGo4HdbgcANDc3Y8KECVi1ahVEUcSAAQMQExODIUOG4LnnnsMbb7yB5uZmP/8GxNdi98B5Vj/OofpxDoMD51H9OIfBgesQ9eNrkUjd/BJok97o+/btixkzZuDJJ5/EFVdcgfXr12P9+vUAAIvFAgC44oorcOONN+Lzzz/HDz/8IL+5bNy4ESNGjMCIESP88St0e5xD9eMcBgfOo/pxDoMHF0bqxtdi98B5Vj/OofpxDoMD51H9OIfBg+sQdeNrkShI+DJ97vDhw2elIlutVlEURXHfvn3iFVdcIc6ePVv+mZQae/z4cfGmm24Sw8PDxXnz5okLFy4U4+Li5PrATG/2Hc6h+nEOgwPnUf04h8FDmpstW7aIv//978VTp06JTz31lDhgwABx3bp1oiiKotlsFkVRFOvr68UHHnhAFARB/P777+XHeOaZZ8SLLrqoTYkX8g2+FrsHzrP6cQ7Vj3MYHDiP6sc5DB5ch6gbX4tEwcUngbYPP/xQ7NWrlzhgwABx7Nix4rJly+SftX7xv/nmm+KgQYPEN998UxTFljcXyX/+8x/xt7/9rXjrrbeKhw4d8sXQyYVzqH6cw+DAeVQ/zmHw4MJI3fha7B44z+rHOVQ/zmFw4DyqH+cweHAdom58LRIFJ68H2r777juxV69e4ssvvyx+88034kMPPSTq9Xrxtddekxs0Sm8UhYWF4u233y6OGTNGrKurE0VRFC0Wi7eHSOfBOVQ/zmFw4DyqH+cwOHBhpH58LXYPnGf14xyqH+cwOHAe1Y9zGBy4DlE/vhaJgpfXAm3SG/xjjz0m5uTktHkjuOeee8TRo0eLq1atOut+//vf/8TRo0eLjz76qLh7925xzpw5Yn5+vreGSefAOVQ/zmFw4DyqH+cweHBhpG58LXYPnGf14xyqH+cwOHAe1Y9zGDy4DlE3vhaJgp/GW73fBEEAABw4cABZWVnQ6/WwWq0AgL/97W8ICQnBZ599htLSUgAtjR8vvPBCjB07Fn/5y1+Qk5MDq9WKpKQkbw2TzoFzqH6cw+DAeVQ/zqH6ia5m4Zs3b0Z8fDwWL16MSy65BM8++ywWL16M1157Dd988w0AQKfTAQDS09Nx1VVXQRRFPPPMM9izZw/mzZuHgoICv/0e3R1fi90D51n9OIfqxzkMDpxH9eMcqh/XIcGBr0WibkCpiN13330n3nfffeLzzz8v/vLLL/L3X3vtNTEyMlKuByxF7F977TWxf//+4vr16+Xb1tfXi88//7yo1WrFadOmiXv27FFqeNQJnEP14xwGB86j+nEOg9e1114rLliwQBTFlvmrqqoSJ02aJN58881iSUmJKIotfRAaGhrEe+65RxQEQdTpdOIll1wiNjc3+2fw3RBfi90D51n9OIfqxzkMDpxH9eMcBi+uQ9SFr0Wi7qfLgbbi4mJxzpw5YlJSkrho0SJx6NChYnR0tPwmkpeXJ6anp4uPPPKIKIqiaDab5fumpKSIzz//vPzv/fv3i+PGjRPffffdrg6L3MA5VD/OYXDgPKof5zB4cGGkbnwtdg+cZ/XjHKof5zA4cB7Vj3MYPLgOUTe+Fom6ry4F2hoaGsSbb75ZvPbaa8Xjx4/L3x87dqx4yy23iKIoiiaTSfzb3/4mhoaGyjVkpbq0U6dOFe+4446uDIG6iHOofpzD4MB5VD/OYXDgwkj9+FrsHjjP6sc5VD/OYXDgPKof5zA4cB2ifnwtEnVvXerRFhYWBqPRiFtuuQW9e/eGzWYDAMyePRsHDx6EKIqIjIzE9ddfj1GjRmHBggU4deoUBEFAfn4+ysvLMXfuXCUqYJKHOIfqxzkMDpxH9eMcql9jYyOWLl2K8PBwbNmyBe+//z727NmDAQMG4JVXXgEApKam4u6778YzzzyDgoICGAwGuW/CgAEDsH//fvnxBg0ahC1btuDGG2/0y+/TXfG12D1wntWPc6h+nMPgwHlUP86h+nEdEhz4WiTq3gRRelf2kNVqhV6vBwA4HA5oNBosWrQI4eHheO211+TbFRUVYdq0abDZbBg9ejR+/vlnZGdnY/ny5UhOTu7ab0FdwjlUP85hcOA8qh/nUP3uuusuXHXVVbj00kths9mg0+nw2GOP4euvv8bmzZshCAJOnDiBG2+8EXa7HStWrEDPnj2Rn5+PSy+9FE8//TQuu+wyf/8a3R5fi90D51n9OIfqxzkMDpxH9eMcqh/XIcGBr0Wi7qvLgbb2TJo0CYsXL8bNN98Mh8MBANBoNDh69Chyc3Pxyy+/YPjw4bj55puVfmpSCOdQ/TiHwYHzqH6cQ3Xhwih48bXYPXCe1Y9zqH6cw+DAeVQ/zqG6cB0SvPhaJOoeFA+0HT9+HBMnTsSXX36JnJwcAIDFYoHBYFDyaciLOIfqxzkMDpxH9eMcBgcujNSPr8XugfOsfpxD9eMcBgfOo/pxDoMD1yHqx9ciUffRpR5trUnxuo0bNyIiIkJ+83jsscfw61//GuXl5Uo9FXkJ51D9OIfBgfOofpzD4HH8+HEcPXoUQ4YMAeBc2Eq19vv27Ytrr70Wzz33HBe3AYqvxe6B86x+nEP14xwGB86j+nEOgwfXIerG1yJR96NT6oEEQQAAbN26FfPnz8f333+PO++8E42NjXjvvfeQlJSk1FORl3AO1Y9zGBw4j+rHOVQ/URQhCEK7C6PS0lI89thjnEcV4Guxe+A8qx/nUP04h8GB86h+nEP14zokOPC1SNT9KFo6srm5GUOHDsWxY8dgMBjw2GOP4f/9v/+n1MOTD3AO1Y9zGBw4j+rHOQwO9957L8LDwzFz5sw2C6OLL77Y30OjTuJrsXvgPKsf51D9OIfBgfOofpzD4MB1iPrxtUjUvSjeo+2iiy5Cv3798NxzzyEkJETJhyYf4RyqH+cwOHAe1Y9zqG5cGAUPvha7B86z+nEO1Y9zGBw4j+rHOVQ3rkOCB1+LRN2H4oE2u90OrVar5EOSj3EO1Y9zGBw4j+rHOVQ/LoyCA1+L3QPnWf04h+rHOQwOnEf14xyqH9chwYGvRaLuQ/FAGxEREVGw4MKIiIiIiIh8jesQIiJ1YaCNiIiIiIiIiIiIiIiIyAMafw+AiIiIiIiIiIiIiIiISI0YaCMiIiIiIiIiIiIiIiLyAANtRERERERERERERERERB5goI2IiIiIiIiIiIiIiIjIAwy0EREREREREREREREREXmAgTYiIiIiIiIiIiIiIiIiDzDQRkREirnlllswd+5cnz/v22+/DUEQIAgCHnjggXPetlevXnjhhRc69bjTpk2TH3fXrl1dHicRERERESmP6xAiIiLyJ52/B0BEROogCMI5f/7oo4/ixRdfhCiKPhpRW1FRUcjLy0N4eLhij7lq1SocO3YMY8eOVewxiYiIiIio87gOISIiokDHQBsREXVKSUmJ/P8//PBD/OlPf0JeXp78vYiICERERPhjaACcC/CUlBRFHzMuLg4mk0nRxyQiIiIios7jOoSIiIgCHUtHEhFRp6SkpMhf0dHR8oJS+oqIiDirZMu0adNw33334YEHHkBsbCySk5Px+uuvo6GhAbfeeisiIyPRt29ffP31122ea9++fZg1axYiIiKQnJyMG2+8ERUVFW6Puby8HJdffjlCQ0PRu3dvfPDBB21+Looi/vznPyMzMxNGoxFpaWm4//77PfrvQ0REREREyuM6hIiIiAIdA21ERORV77zzDhISErB161bcd999uPvuu3HNNddg4sSJ2LFjBy6++GLceOONaGxsBADU1NRg+vTpGDlyJLZv345vvvkGZWVlWLBggdvPfcstt6CgoADr1q3Dxx9/jH//+98oLy+Xf/7JJ5/g+eefx6uvvoojR47g008/xdChQxX73YmIiIiIyD+4DiEiIiJfYelIIiLyquHDh+OPf/wjAGDp0qV44oknkJCQgMWLFwMA/vSnP+GVV17Bnj17MH78ePzrX//CyJEj8Y9//EN+jDfffBMZGRk4fPgw+vfv36nnPXz4ML7++mts3boVY8aMAQAsW7YMAwcOlG+Tn5+PlJQUzJw5E3q9HpmZmeyDQEREREQUBLgOISIiIl9hRhsREXnVsGHD5P+v1WoRHx/f5rRmcnIyAMgnPHfv3o1169bJvRYiIiKQnZ0NADh27Finn/fgwYPQ6XTIycmRv5ednY2YmBj539dccw2amprQp08fLF68GKtXr4bNZvPo9yQiIiIiosDBdQgRERH5CjPaiIjIq/R6fZt/C4LQ5nuCIAAAHA4HAKC+vh6XX345nnzyybMeKzU1VdGxZWRkIC8vDz/88AO+//573HPPPXj66aexYcOGs8ZNRERERETqwXUIERER+QoDbUREFFBGjRqFTz75BL169YJO5/nHVHZ2Nmw2G3Jzc+WSLXl5eaipqWlzu9DQUFx++eW4/PLLsWTJEmRnZ2Pv3r0YNWpUV34NIiIiIiJSEa5DiIiIyFMsHUlERAFlyZIlqKqqwsKFC7Ft2zYcO3YM3377LW699VbY7fZOP86AAQNw6aWX4q677sIvv/yC3Nxc3HHHHQgNDZVv8/bbb2PZsmXYt28fjh8/jvfffx+hoaHo2bOnN341IiIiIiIKUFyHEBERkacYaCMiooCSlpaGTZs2wW634+KLL8bQoUPxwAMPICYmBhqNex9bb731FtLS0jB16lTMmzcPd955J5KSkuSfx8TE4PXXX8cFF1yAYcOG4YcffsAXX3yB+Ph4pX8tIiIiIiIKYFyHEBERkacEURRFfw+CiIioK95++2088MADZ5VjUcLJkyfRu3dv7Ny5EyNGjFD88YmIiIiISJ24DiEiIiKAGW1ERBQkamtrERERgf/3//6fYo85a9YsDB48WLHHIyIiIiKi4MJ1CBERETGjjYiIVK+urg5lZWUAnGVYEhISFHncoqIiNDU1AQAyMzNhMBgUeVwiIiIiIlI/rkOIiIgIYKCNiIiIiIiIiIiIiIiIyCMsHUlERERERERERERERETkAQbaiIiIiIiIiIiIiIiIiDzAQBsRERERERERERERERGRBxhoIyIiIiIiIiIiIiIiIvIAA21EREREREREREREREREHmCgjYiIiIjo/7dnxwIAAAAAg/yt57C7NAIAAAAYRBsAAAAAAAAMog0AAAAAAACGAMnRTnWrXt/+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x350 with 2 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(df, Y_hat_df, max_insample_length=24*5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "For most applications, models with temporal normalization (section 4) produced more accurate forecasts than time series scaling (section 3). However, with temporal normalization models lose the information of the relative level between different windows. In some cases this global information within time series is crucial, for instance when an exogenous variables contains the dosage of a medication. In these cases, time series scaling (section 3) is preferred.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- [Kin G. Olivares, David Luo, Cristian Challu, Stefania La Vattiata, Max Mergenthaler, Artur Dubrawski (2023). \"HINT: Hierarchical Mixture Networks For Coherent Probabilistic Forecasting\". International Conference on Machine Learning (ICML). Workshop on Structured Probabilistic Inference & Generative Modeling. Available at https://arxiv.org/abs/2305.07089.](https://arxiv.org/abs/2305.07089)\n",
    "- [Wu, Haixu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. \"Timesnet: Temporal 2d-variation modeling for general time series analysis.\", ICLR 2023](https://openreview.net/forum?id=ju_Uqw384Oq)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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